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Stream Bank and Bar Erosion Contributions and Land Use Stream Bank and Bar Erosion Contributions and Land Use

Influence on Suspended Sediment Loads in Two Ozark Influence on Suspended Sediment Loads in Two Ozark

Watersheds, Southeast Missouri Watersheds, Southeast Missouri

Kayla Ann Coonen Missouri State University, [email protected]

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STREAM BANK AND BAR EROSION CONTRIBUTIONS AND LAND USE

INFLUENCE ON SUSPENDED SEDIMENT LOADS IN TWO OZARK WATERSHEDS,

SOUTHEAST MISSOURI

A Master’s Thesis

Presented to

The Graduate College of

Missouri State University

TEMPLATE

In Partial Fulfillment

Of the Requirements for the Degree

Master of Science, Geospatial Sciences in Geography

By

Kayla Ann Coonen

August 2020

ii

Copyright 2020 by Kayla Ann Coonen

iii



SOUTHEAST MISSOURI

Department of Geography, Geology and Planning

Missouri State University, August 2020

Master of Science

Kayla Coonen

ABSTRACT

In-channel sources and storages of fine-sediment such as in banks and bars can influence

sediment loads and overall geomorphic activity in stream systems. However, in-channel

processes and effects on sediment load are rarely quantified in geomorphic or water quality

studies. This study uses a sediment budget approach to assess the influence of bank erosion and

bar deposition on fine sediment loads in Mineral Fork (491 km2) and Mill Creek (133 km2)

watersheds located in the Ozark Highlands in Washington County, Missouri. These watersheds

were disturbed by historical lead and barite mining which included the construction of large

tailings dams across headwater valleys. USEPA’s Spreadsheet Tool for Estimating Pollutant

Loads (STEPL) was used to quantify suspended sediment delivery from upland areas and assess

land use-load relationships. Aerial photographs from 1995 and 2015 were used to identify spatial

patterns of erosion and deposition in bank and bar forms. LiDAR was used to characterize the

channel network and determine bank and bar heights. Field measurements were used to ground-

truth bank and bar heights and fine-sediment composition of alluvial deposits. Historical tailings

dams capture runoff from 27% of Mineral Fork and 28% of Mill Creek drainage areas, trapping

38% and 26% of the suspended sediment load annually, respectively. The total annual sediment

yield for Mineral Fork watershed was 92 Mg/km2/yr with 55% released by bank erosion and

<1% reduced by bar storage. The sediment yield for Mill Creek was 99 Mg/km2/yr with 33%

released by bank erosion and 24% reduced by bar storage. These results indicate that in-channel

processes are important contributors to sediment yields in these watersheds.

KEYWORDS: Bank Erosion, Mining, Sediment Budgets, STEPL, Nonpoint Source Pollution,

Ozark Highlands, Missouri

iv



SOUTHEAST MISSOURI

By

Kayla Ann Coonen

A Master’s Thesis

Submitted to the Graduate College

Of Missouri State University

In Partial Fulfillment of the Requirements

For the Degree of Master of Science, Geospatial Sciences in Geography

August 2020

Approved:

Robert T. Pavlowsky, Ph.D., Thesis Committee Chair

Toby J. Dogwiler, Ph.D., Committee Member

Marc R. Owen, MS, Committee Member

Julie Masterson, Ph.D., Dean of the Graduate College

In the interest of academic freedom and the principle of free speech, approval of this thesis

indicates the format is acceptable and meets the academic criteria for the discipline as

determined by the faculty that constitute the thesis committee. The content and views expressed

in this thesis are those of the student-scholar and are not endorsed by Missouri State University,

its Graduate College, or its employees.

v

ACKNOWLEDGEMENTS

I thank my advisor Dr. Robert Pavlowsky for his expertise, advising, and providing me

with so many great opportunities during my time at Missouri State University. Throughout the

course of this research and my graduate studies. Thank my committee member Marc Owen for

his continued mentoring, knowledge, and support throughout the course of my graduate

assistantship. I also thank my committee member Dr. Toby Dogwiler for his support, editorial

assistance, and technological knowledge as I switched to complete my thesis online.

I thank all of the Ozarks Environmental and Water Resources Institute (OEWRI) staff

(Tyler Pursley, Triston Rice, Josh Hess, Michael Ferguson, Max Hillermann, Jean Fehr, Hannah

Eades, Sarah LeTarte, Hannah Adams, Katy Reminga, and Kelly Rose) for helping me make this

research possible with field and laboratory work, and for all the support during my graduate

studies. I also want to thank the OEWRI staff for the amazing memories at MSU, from the

unproductive lunch hour to the 2:30 coffee breaks. And a special thanks to Sierra Casagrand for

the help in the field, hours of helping me sieve samples, and most importantly the constant edits

and emotional support you provided towards the end of my studies.

I am tremendously grateful to my parents (Julie Wild and Chris Coonen) and step-parents

(Larry Wild and Teresa Coonen) who have given me endless support through all of my years of

schooling. I appreciated the care packages, all the phones calls that ended in “I’m Proud of You,”

and continued assistance for special trips home. Additionally, I thank my brother, Matthew

Coonen, for helping me, even when I was hours away. I could not have gotten to this point in my

academic career without all of you keeping my spirits high while I was away from home.

Lastly, I thank the OEWRI for my graduate assistantship, partial funding for this research

through funding by USEPA Cooperative agreement number V97751001 Big River Riffle-Basin

Monitoring Project for the Big River Superfund Site, and funding for my graduate assistantship

through the USDA-NRCS Missouri Agricultural Watersheds Assessment Project award number

R186424XXXXC030.

vi

TABLE OF CONTENTS

Introduction Page 1

Channel geomorphology influence on sediment loads Page 2 Bank erosion assessments Page 5 Channel sediment concerns in the Ozark Highlands Page 7 Purpose and objectives Page 8

Benefits of this study Page 10

Study Area Page 15

Location Page 15

Geology and soils Page 16

Climate and hydrology Page 17 Settlement and land use history Page 18

Methods Page 28

Channel bank and bar assessments Page 28 Spatial datasets Page 32 Geomorphic spatial analysis at the reach-scale Page 36 Sediment budget development Page 37

Results & Discussion Page 51

Channel delineation and network analysis Page 51

Bank and bar deposit assessment Page 52

Cell-level channel characteristics and trends Page 56 Sediment budget Page 65

Conclusions Page 99

References Page 103

Appendices Page 114 Appendix A. Drainage area and discharge relationships for 32

USGS gaging stations near the study watershed. Page 114

Appendix B. Field assessments. Page 116 Appendix C. Sediment sample information. Page 119 Appendix D. Cell location information in Mineral Fork. Page 120 Appendix E. Cell location information in Mill Creek. Page 128 Appendix F. STEPL inputs. Page 130 Appendix G. USLE inputs for STEPL. Page 131 Appendix H. Large dams in Mineral Fork and Mill Creek

watersheds. Page 132

vii

LIST OF TABLES

Table 1. Floodplain deposition rates in the Ozark Highlands and

Midwest Driftless Area.

Page 11

Table 2. Bank erosion contributions to suspended sediment loads from

watersheds in the U.S. Page 12

Table 3. Sediment yields from selected watersheds in the U.S. Page 13

Table 4. 12-Digit HUC watersheds within Mineral Fork and Mill

Creek. Page 22

Table 5. Descriptions of bedrock geology in Mineral Fork and Mill

Creek watersheds. Page 23

Table 6. Alluvial soils within Mineral Fork and Mill Creek watersheds. Page 23 Table 7. Change in land cover from 2010 to 2017 without mined land. Page 24

Table 8. Aerial photograph characteristics. Page 42 Table 9. Definition of variables for deposit volume and mass

calculations. Page 42

Table 10. Description of sediment budget terms. Page 43 Table 11. Total length of stream network by stream order delineation in

Mineral Fork. Page 73

Table 12. Total length of stream network by stream order delineation in

Mill Creek. Page 73

Table 13. Comparison of antecedent flood Conditions five years prior

to aerial photograph dates. Page 74

Table 14. Active channel width reach assessment. Page 74

Table 15. Height distribution per HUC-12 per stream order for bank

erosion. Page 75

Table 16. Height distribution per HUC-12 per stream order for bank

deposition. Page 76

viii

Table 17. Height distribution per HUC-12 per stream order for bar

erosion. Page 77

Table 18. Height distribution per HUC-12 per stream order for bar

deposition. Page 78

Table 19. Average cell mass for bank erosion. Page 79 Table 20. Average cell mass for bank deposition. Page 80 Table 21. In-channel sediment budget. Page 81

Table 22. Average cell mass for bar erosion. Page 82 Table 23. Average cell mass for bar deposition. Page 83 Table 24. Average cell mass for net in-channel supply. Page 84 Table 25. Sediment load with above dam contributions. Page 85 Table 26. Sediment load below dams. Page 85 Table 27. Sediment budget for below dam area. Page 86 Table 28. Suspended sediment loads below dams from upland erosion

by land use. Page 87

ix

LIST OF FIGURES

Figure 1. Model of sediment storage and remobilization within the

channel.

Page 14

Figure 2. Mineral Fork and Mill Creek watersheds within the Big River

in relation to the Old Lead Belt and the Barite Mining District.

Page 25

Figure 3. Geology of Mineral Fork and Mill Creek. Page 26

Figure 4. Major mined areas in Mineral Fork and Mill Creek

watersheds.

Page 26

Figure 5. Land Use classification from USDA-NASS 2017 for Mineral

Fork and Mill Creek watersheds.

Page 27

Figure 6. Location of field assessment sites. Page 44

Figure 7. Coarse unit thickness in bank deposits. Page 45

Figure 8. Texture of bank deposits. Page 45

Figure 9. Application of error to active channel features. Page 46

Figure 10. Digitized and delineated stream network. Page 47

Figure 11. Comparison of LiDAR bank height to field bank height. Page 47

Figure 12. Cell distribution below dams by stream order. Page 48

Figure 13. Deposit volume to fine sediment mass conversion by cell. Page 49

Figure 14. Mining areas classified as forest from the 2015 DOQQ

aerial photo.

Page 50

Figure 15. Number of cells in each subwatershed by stream order

below dams.

Page 87

Figure 16. Planform analysis for Mineral Fork with bar and bank

erosion and polygons.

Page 88

Figure 17. Planform analysis for Mill Creek with bar and bank erosion

and polygons.

Page 89

Figure 18. Annual peak flood record (1950-2019, 70 years). Page 90

x

Figure 19. Active channel width reach assessment. Page 90

Figure 20. Average bank and bar heights. Page 91

Figure 21. Average active channel width in 2015 and 1995. Page 91

Figure 22. Active channel width change from 1995 to 2015. Page 92

Figure 23. Average bar width in 2015 and 1995. Page 93

Figure 24. Percent bar width change from 1995 to 2015. Page 93

Figure 25. Mass of fine sediment from in-channel contributions. Page 94

Figure 26. Cells highlighting no erosion, erosion, and high erosion cells

that make up 25% of the bank erosion mass.

Page 94

Figure 27. Cells highlighting no erosion, erosion, and high erosion cells

that make up 25% of the bar erosion mass.

Page 95

Figure 28. Cells highlighting deposition, erosion, and high erosion cells

that make up 25% of the erosion mass.

Page 95

Figure 29. Alternating pattern of erosion and deposition upstream to

downstream in the Mill Creek watershed.

Page 96

Figure 30. Mass sediment budget for Mineral Fork watershed (Mg/yr). Page 96

Figure 31. Mass sediment budget for Mill Creek watershed (Mg/yr). Page 97

Figure 32. In-channel contributions to sediment loads. (A) Bank

erosion compared to upland erosion loads; (B) Bar erosion

compared to upland erosion loads; and (C) In-channel load

contribution to total load.

Page 98

1

INTRODUCTION

Eroding stream banks can be significant sources of fine sediment to streams that increase

water quality concerns, typically supplying 20% to 80% of the total suspended sediment load at

the watershed outlet (Harden et al., 2009; De Rose and Basher, 2011; Kessler et al., 2013;

Spiekermann et al., 2017). Bank erosion can occur gradually as the channel migrates back and

forth across the valley floor over relatively long periods of time (Figure 1) (Trimble, 1983;

Kondolf, 1997; De Rose and Basher, 2011). In many streams, it is a natural process for point bar

and floodplain deposition to be on the opposite side of cut-bank erosion in order to maintain a

constant channel width and shape (Kondolf, 1997). However, watershed-scale disturbances can

increase flood discharge, bank failures, or sediment loads, which can accelerate bank erosion

rates greater than 2 m/yr in smaller streams (Harden et al., 2009; Rhoades et al., 2009; De Rose

and Basher, 2011; Martin and Pavlowsky, 2011; Kessler et al., 2013; Janes et al., 2017;

Spiekermann et al., 2017).

Once eroded sediment is in transport, it is usually deposited relatively soon on channel

beds, bars, and floodplains. Bank and floodplain sediment can remain in storage for a year to

centuries before being remobilized again (Meade, 1982). Bank erosion can also exacerbate

channel instability by causing channel instability through channel widening, flow turbulence

along bends, and release of coarse sediment (Ferguson et al., 2003; Michalkova et al., 2011). The

additional coarse sediment load can accelerate bar deposition and create flow deflection and

more erosive currents in the channel (Jacobson and Primm, 1997; Blanckaert, 2011; Martin and

Pavlowsky, 2011). Therefore, bank erosion processes can be both a cause and effect of the

geomorphic and sediment characteristics of a stream channel.

2

The flood regime of a stream system will tend to control its shape and erosional potential

(Rosgen, 1994). High rates of bank erosion are commonly caused by high-magnitude, low-

frequency floods. However, flood effects on sand and gravel bars in rivers are not as well

understood (Hagstrom et al., 2018). Nevertheless, assessments of bank erosion rates and their

causal factors have been described in the literature (e.g., De Rose and Basher, 2011; Kessler et

al., 2013; Janes et al., 2017; and Spiekermann et al., 2017). Many studies of bank and bar

behavior have been completed for individual stream reaches. However, there have been fewer

attempts to quantify the spatial distribution of bank erosion inputs from different locations within

the channel network in relation to bank deposition and other in-channel sediment storages such

as bench and bar deposits (Panfil and Jacobson, 2001; Martin and Pavlowsky, 2011; Owen et al.,

2011).

Channel geomorphology influence on sediment loads

Sediment is recognized as the number one nonpoint source pollutant in the United States,

with 70% of fine sediment in impaired streams coming from past and present human activities

(Brown and Froemke, 2012; USEPA, 2018a). However, the important role of stream

geomorphology as a natural control on suspended loads, such as adjustments in channel form and

sediment storage, is commonly overlooked in nonpoint source (NPS) pollution models that

assess water quality trends in watersheds (Nejadhashemi et al., 2011; Fox et al., 2016; Beck et

al., 2018). Geomorphic processes involving the formation and adjustments of fluvial landforms

by sediment erosion and deposition can significantly change stream sediment loads at timescales

from years to decades (Jacobson and Gran, 1999; Knighton, 1998; Hession et al., 2003).

Increased runoff and bed instability can cause channel enlargement and the release of sediment

3

to the watershed, while impoundments and floodplain deposition can trap sediments (Ward and

Elliot, 1995; Knighton, 1998; James, 2013). Additionally, floodplains can be a major sink for

fine sediment with annual sedimentation rates typically ranging from 0.1 to 15 cm/yr (Table 1).

In some watersheds, the sediment delivery rates to streams have decreased significantly since the

period of highest land use disturbances that occurred almost a century ago due to improved land

management practices, bank stability structures, and the regrowth of vegetation (Trimble,

1983,1999; Troeh et al., 2004). Conversely, bank erosion inputs and channel deposition can

increase after a period of channel recovery or the implementation of stabilization practices in

some cases (Trimble, 1999; Schenk and Hupp, 2009; Gillespie et al., 2018). Sediment budgets

measure the amount of sediment eroded and stored in different sections of a watershed (i.e.

uplands, headwaters, floodplains, and in-channel processes) (Trimble, 1999; Lauer et al., 2017).

Sediment budgets are important assessment tools used to evaluate sediment fluxes and storage in

a watershed by quantifying the amounts of sediment being stored in and eroded from different

landform components (Phillips, 1991; Beach, 1994; Trimble, 2009).

An important contribution to a sediment budget can be the release of excess sediment

previously deposited on floodplains. Historical land use practices associated with widespread

agricultural settlement including the clearing of forests, soil disturbance by cultivation, and

construction of road networks released large volumes of fine sediment from hillslopes for

deposition on floodplains in the Midwest USA (Knox, 1972; Trimble, 1983). These “legacy”

sediment deposits were stored in floodplains and other valley floor locations at depths up to

several meters (Knox, 1972; Lecce, 1997; Wilkinson and McElroy B.J, 2007; Owen et al., 2011;

James, 2013; Donovan et al., 2015; Pavlowsky et al., 2017). Flow obstructions, such as mill

dams, increased the rate of legacy sediment deposition in some regions (Trimble and Lund,

4

1982; Walter and Merritts, 2008; Schenk and Hupp, 2009). In tributaries, the higher banks

formed by legacy deposits produced deeper flows that were able to generate higher stream

powers and increase bank erosion rates for more than 50 years (Knox, 1987; Lecce, 1997; Ward

et al., 2016). In mining districts, where relatively large volumes of tailings were introduced to

nearby rivers, legacy floodplain deposits were able to store metal-contaminated sediment from

100 to 1,000 years until remobilized by bank erosion (Marron, 1992; Rhoades et al., 2009; Lecce

and Pavlowsky, 2014). Even after conservation practices were implemented to reduce soil

erosion, legacy sediment stored in valleys was still being remobilized by bank erosion (Trimble,

1999; Troeh et al., 2004).

Typically, hydrologic watershed models are used to determine suspended sediment loads

from predicted upland soil erosion yields with the relative contribution to stream loads that are

decreasing with downstream distance (Brierley et al., 2006; Baartman et al., 2013; James, 2013).

In general, suspended sediment loads tend to increase with rainfall amount, intensity, and land

use characteristics that increase storm water routing, runoff rates and erosion (Lawler, 1993;

Brown and Froemke, 2010, 2012; Emili and Greene, 2013; USEPA, 2018b). Trimble (1983)

assessed sediment contributions to the Coon Creek watershed, Wisconsin from upland erosion,

main valleys, and tributaries. The sheet and rill erosion of uplands in Coon Creek were estimated

using the universal soil loss equation (USLE) in the form: A = RKLSCP, where A is equal to the

amount of soil loss in tons per acre per year, R is the rainfall factor, Kf is the soil erodibility

factor, L is the slope-length factor, S is the slope-gradient factor (S), C is the land use and land

management factor, and P is the erosion control practice factor (Trimble and Lund, 1982; Troeh

et al., 2004). Today, models like the Spreadsheet Tool for Estimating Pollutant Loads (STEPL)

incorporate the USLE into calculations of sediment load outputs from watersheds with variable

5

land uses and soil cover (Nejadhashemi et al., 2011; Park et al., 2014; WiDNR, 2014; Liu et al.,

2017). However, stream bed and bank erosion inputs are rarely evaluated directly in watershed

models and are only used to balance variations in modeled tributary inputs and assumed channel

conditions (Trimble, 1999; Bracken et al., 2015). The literature reported that streambank erosion

and other in-channel contributions such as bed material accounted for 7-92% of the annual

suspended sediment load in a watershed (Table 2) (Fox et al., 2016).

Bank erosion assessments

Over the past several decades, the methods for measuring bank erosion rates have

advanced from field work to GIS methods (Lawler, 1993). Field methods have long been

employed to study bank erosion (Leopold, 1973). Cross-sectional surveys can be used to

measure active channel widths and areas (Xia et al., 2014). Additionally, repeat cross-sectional

surveys over time can be used to assess bank erosion rates between floods (Julian and Torres,

2006). Erosion pins are deployed to estimate bank erosion rates where rebar pins are inserted into

the bank, leaving a known length exposed to provide a ‘benchmark’ against which bank erosion

can be measured as they become more exposed (Couper et al., 2002; Harden et al., 2009;

Foucher et al., 2017; Beck et al., 2018). Problems can arise with the use of erosion pins to

evaluate short-term (months to years) bank erosion rates since negative values can result from

the deposition of sediment during high flows, upper bank failures covering lower bank pins, and

human interference (Couper et al., 2002). More frequent observations can reduce erosion pin

error, but also add more cost and effort for the project (Couper et al., 2002; Xia et al., 2014).

Historical aerial photography is more commonly used now to track bank locations over

time to determine streambank erosion rates (Rhoades et al., 2009; Martin and Pavlowsky, 2011).

6

Typically, bank line locations are digitized and compared between two dates of aerial

photographs (Mount and Louis, 2005; De Rose and Basher, 2011; Spiekermann et al., 2017).

However, digitizing needs to be completed at a relatively large and consistent scale of 1:1,000 or

1:600 to reduce worker and photograph errors during manual digitizing (Rhoades et al., 2009;

Spiekermann et al., 2017). When planform surveys for different years are combined to identify

areas of erosion and deposition in the channel, tiny polygon “slivers” may occur and these are

likely insignificant for use as a survey result. Those areas can be identified by spatial error

analysis and ignored for use in erosion inventories (De Rose and Basher, 2011). In general, while

digitizing errors do occur, they are assumed to be random and cancel one another out (Mount and

Louis, 2005; De Rose and Basher, 2011; Spiekermann et al., 2017). However, during

georeferencing the root-mean-square error (RMSE) is calculated for distances between ground-

points compared between two photographs to evaluate spatial errors for feature measurements

(Mount and Louis, 2005; Janes et al., 2017). The typical range for RMSE errors in these studies

was two to five meters for the georeferenced aerial photographs.

The use of aerial photographs limits assessment of the channel migration process to a

two-dimensional result. By incorporating a high resolution light detection and ranging (LiDAR)

derived digital elevation model (DEM), bank heights can be estimated and used to calculate a

volume for the eroded banks (Rhoades et al., 2009; Kessler et al., 2013). The main problem

associated with incorporating LiDAR to the aerial photography is having data sets from the same

time periods. The collection data for the photographs and LiDAR are usually months or years

apart, potentially altering the actual geomorphic characteristics of the period being measured to

some degree (Kessler et al., 2012; Spiekermann et al., 2017). LiDAR also has errors depending

on how the dataset was mosaiced from different flight series and the degree to which water

7

surface reflections can give false heights on streambanks. Water reflection can be corrected in

streams by using an assumed channel geometry or field data to correct the bank heights (Kessler

et al., 2012; Podhoranyi and Fedorcak, 2014).

Channel sediment concerns in the Ozark Highlands

Historical farm and logging land clearing by European settlers caused increased soil

erosion on uplands and in tributary valleys increasing fine and coarse sediment loads in streams

of the Ozark Highlands of Missouri (Jacobson, 1995; Jacobson and Gran, 1999; Panfil and

Jacobson, 2001; Owen et al., 2011; Reminga, 2019). These disturbances were magnified by

prevailing topographic conditions including rolling hills with steep slopes, narrow valleys, and

streams with gravel bed loads (Nigh and Schroeder, 2002). Over several meters of silty sediment

were deposited on floodplains along some rivers that drained agricultural areas in the Ozark

Highlands (Owen et al., 2011; Pavlowsky et al., 2017). However, these land use changes also

increased the deposition rate and supply of coarser sand and gravel main channels and their

tributaries (Jacobson and Primm, 1997; Jacobson and Gran, 1999; Martin and Pavlowsky, 2011).

The coarse sediment deposits were located in the channel within persistent disturbance zones that

were reactivated by large floods (Panfil and Jacobson, 2001; Lauer et al., 2017). Present-day

gravel storages in the channel relate more to the influence of historical disturbances rather than

recent land use impacts (Panfil and Jacobson, 2001). Nevertheless, both legacy sediment and

recent gravel bars can increase channel instability in disturbance zones. These geomorphic

conditions can increase bank erosion rates or the storage rate of fine sediment on bars or benches

along the river channel (Martin and Pavlowsky, 2011; Lauer et al., 2017). Therefore, fine-

8

grained sediment storage and remobilization rates should be included to calculate accurate

sediment loads and sediment budgets in Ozark watersheds.

In the Ozark Highlands, there are no published studies that attempt to link the sediment

being stored and transported through a stream network to stream loads. One related example

would be the role that mining sediment storage plays in controlling sediment contamination

trends in the Big River, southeast Missouri which was contaminated by large-scale lead mining

from 1895 to 1972 (Pavlowsky et al., 2010, 2017). Another related example used the floodplain

core records to understand how legacy sediment deposition rates related to historical land use

changes along the James River, southwest Missouri (Owen et al., 2011). While there are several

studies that provide some information about suspended sediment yields from Missouri

watersheds, none describe how sediment is being routed through the channel system (Table 3). In

addition, there is a gap in knowledge in our understanding of how channel processes, sediment

storage, and land use factors control suspended sediment loads and associated pollutants.

Further, watershed managers in southeast Missouri are concerned about channel instability, bank

erosion, and sediment contamination by lead from mining operations since the 1700s in rural

watersheds with a long history of soil disturbance (MDNR, 2006, 2008, 2014; Mugel, 2017)

Purpose and objectives

The purpose of this study is to assess and evaluate the contributions of bank and bar

erosion to annual sediment loads of Mineral Fork (491 km2) and Mill Creek (133 km2)

watersheds in the Ozark Highlands, Missouri. Since there are no published studies available for

the Ozarks, this study will fill this gap and offer a methodology for assessing the watershed

trends in channel erosion where management efforts are needed to reduce bank erosion inputs.

9

Bank erosion rates were determined using historical aerial photography and LiDAR data to

evaluate to sediment loads derived from a simple NPS watershed model, the Spreadsheet Tool

for Estimating Pollutant Loads (STEPL) (Tetra Tech, 2018; USEPA, 2019). These watersheds

have been experiencing a decrease in water quality due to runoff and soil disturbances from

historical land-clearing and lead and barite mining, and cattle grazing agriculture (Jacobson and

Primm, 1997; Mugel, 2017; Schumacher and Smith, 2018; USEPA, 2018a). Environmental

managers are concerned about excess sedimentation in Ozark streams from bank, sheet, and rill

erosion (Adamski et al., 1995; MDNR, 2014, 2016, 2018).

The study watersheds are representative of landscape characteristics and stream network

conditions of the Salem Plateau, the largest sub-region of the Ozark Highlands (Nigh and

Schroeder, 2002; USDA-NRCS, 2006). They are affected by rural conditions including low

income, failure of septic systems, and grazing agriculture on slopes and within riparian corridors

(Jacobson and Primm, 1997; MDNR, 2014; USDA, 2017). Large barite tailings ponds and dams

built between 1935-1991 to trap mine tailings and eroding soil are distributed throughout the

middle and lower portions of these watersheds (Mugel, 2017; MSDIS, 2019). Over 27% of

Mineral Fork and 28% of Mill Creek watersheds are composed of obstructed drainage areas by

tailings dams up to 31 m high (MSDIS, 2019). Given that these dams trap 100% of the sediment

and water from above drainage areas, they may affect sediment loads downstream. Moreover,

mining disturbed lands can cause stream channel instability with excessive erosion and

sedimentation (Mugel, 2017).

Like most of the Ozark Highlands, Mineral Fork and Mill Creek transport a bedload of

sand and gravel that form bar complexes associated with local channel aggradation and high

rates of bank erosion and channel widening (Martin and Pavlowsky, 2011). These geomorphic

10

characteristics suggest that bank erosion and bar sedimentation may play an important role in

fine sediment supply in these watersheds. The specific objectives of this study are:

1) Assess geomorphic characteristics of Ozark streams using LiDAR, aerial

photography, and some ground-truthing involved bank measurements in the field;

2) Determine the spatial distribution and mass of fine sediment of channel erosion and

deposition within watersheds; and

3) Develop a sediment budget for each watershed that accounts for the contributions of

channel processes including bank and bar erosion and sedimentation to sediment

loads.

Benefits of this study

Sediment transport and storage can have long-term implications for geomorphic activity

and water quality in streams. This study will contribute to a better understanding of sediment

sources and loads in southeastern Missouri watersheds and aid in evaluating the effects of

historical mining disturbances on channel stability, bank erosion, and sediment loads in Barite

Mining District. Channel processes are often excluded from sediment loads in NPS assessments.

The methodology and results presented in this study will advance our understanding for using

sediment budget analysis to improve NPS assessments in small- to medium-sized watersheds in

the Ozarks. Moreover, it will use fluvial geomorphology concepts to link land use changes to

channel behavior and sediment sources throughout the drainage network. This will provide a

better understanding of the long-term recovery of stream channels from past land disturbances

and anthropogenic sediment inputs.

11

Table 1. Floodplain deposition rates in the Ozark Highlands and Midwest Driftless Area.

Stream Drainage Area

(km2)

Overbank Deposition

Rates (cm/yr) Reference

SW Ozark Highlands

Honey Creek, MO 174 0.6-0.8 Carlson, 1999 James River, MO 637 0.5 Owen et al., 2011

SE Ozark Highlands

Big River, MO 2,500 0.7-1.0 Pavlowsky, 2013

Big River, MO 2,500 0.2-3.4 Keppel et al., 2015

Big River, MO 2,500 0.1-1.0 Pavlowsky and Owen, 2015 Big River, MO 626-2,500 1.3-3.0 Pavlowsky et al., 2017 Big River, MO 2,500 0.8 Jordan, 2019

Big Barren Creek, MO 191 0.2-0.6 Reminga, 2019 Midwest Driftless Area

Kickapoo Valley, WI 1,989 1.52 Happ, 1944 Coon Creek, WI 350 1.5-15.0 Trimble and Lund, 1982

Galena River, WI, IL 340-400 0.8-1.9 Magilligan, 1985 Shullsburg Branch, WI, IL 26 0.3-1.3 Knox, 1987

Galena River, WI, IL 700-170,000 0.5-3.4 Knox, 2006

12

Table 2. Bank erosion contributions to suspended sediment loads from watersheds in the U.S.

Watershed Drainage

Area

(km2)

Suspended

sediment

load from

streambanks

(%)

Reference

Delaware Estuary, PA 35,066 39 Meade, 1982 Sacramento River, CA 7,100 59 USACE, 1983

Obion Forked Deer River, TN 2,000 81 Simon and Hupp, 1986 East Nishnabotna River, IA 2,300 30-40 Odgaard, 1987

Des Moines River, IA 41,000 30-40 Odgaard, 1987 Blue Earth River, MN 1,550 31-44 Sekely et al., 2002

James River, MS 74 78 Simon et al., 2002 Yalobusha River, MS 4,000 90 Simon and Thomas, 2002

Shades Creek, AL 190 71-82 Simon et al., 2004 Blue Earth River, MN 1,550 23-56 Thoma et al., 2005 Le Sueur River, MN 2,880 11-14 Gran et al., 2009

Lower Hinkson Creek, MO 231 67 Huang, 2012 Walnut Creek, IA 52 23-53 Palmer et al., 2014

Piedmont Streams, Baltimore County, MD 155 70 Donovan et al., 2015

13

Table 3. Suspended sediment yields from selected watersheds in the U.S.

Stream Drainage

Area (km2) Sediment Yield

(Mg/km2/yr) Floodplain

Storage (%) Reference

Waterfall Creek, TN 2 13 N/A Hart and Schurger, 2005 Terry Creek, TN 3 8 N/A Hart and Schurger, 2005 Upper Pigeon Roost Creek, TN 9 111 N/A Hart and Schurger, 2005 Wilson's Creek, MO 46 30 N/A Hutchison, 2010 Pearson Creek, MO 54 18 N/A Hutchison, 2010 Upper James River, MO 637 39 N/A Hutchison, 2010 Finley Creek, MO 676 9 N/A Hutchison, 2010 Middle James River, MO 1,197 87 N/A Hutchison, 2010 Le Suer River, MN 2,880 47 N/A Day et al., 2013 Lower Mississippi River, LA 276,460 218 N/A Turner and Rabalais, 2004 Missouri River 1,300,000 48 N/A Turner and Rabalais, 2004 Indian Creek, MN 17 118 65 Beach, 1994 Hay Creek, MN 127 258 87 Beach, 1994 Beaver Creek, MN 144 365 64 Beach, 1994 Coon Creek, WI 360 103 37 Trimble, 1999 Upper Tar, Piedmont, NC 1,119 48 92 Phillips, 1991 Upper Neuse, Piedmont, NC 1,997 64 84 Phillips, 1991 Deep River, Piedmont, NC 3,748 60 91 Phillips, 1991 Haw River, Piedmont, NC 4,217 46 93 Phillips, 1991 Minnesota River, MN 45,000 17 25-50 Lauer et al., 2017

14

Figure 1. Model of sediment storage and remobilization within the channel (Kondolf, 1997).

15

STUDY AREA

Location

The Mineral Fork Watershed (HUC-10# 0714010402) and Mill Creek Watershed (HUC-

12# 071401040301) are located in Washington County, Missouri within the Big River basin

(HUC-8# 07140104) (Figure 2) (USGS, 2018a). In addition to the Mill Creek watershed, Mineral

Fork contains six 12-Digit Hydrologic Unit Code (HUC) watersheds within its boundaries (Table

4). All together these two watersheds contain seven 12-Digit HUC subwatersheds in the study

area as follows: Mineral Fork (MF), Clear Creek-Mineral Fork (CCMF), Old Mines Creek

(OMC), Mine a Breton Creek (MBC), Fourche a Renault (FR), Sunnen Lake-Fourche a Renault

(SLFR), and Mill Creek (MC). The whole Mineral Fork watershed has a drainage area of 491

km2, total channel length of 433 km, and drainage density of 0.88 km/km2. The Mill Creek

watershed has a drainage area of 133 km2, total channel length of 198 km, and drainage density

of 1.49 km/km2. These watersheds drain in the Meramec River Hills Subsection of the of the

Salem Plateau Division of the Ozark Highland Province (Nigh and Schroeder, 2002). Maximum

elevation of headwaters is about 430 masl with base-level elevations near 150 masl at the

confluence of Big River. The local relief in the study area is typically greater than 45 m and rises

to more than 76 m along the major valleys of Mineral Fork (Nigh and Schroeder, 2002). Streams

within this region have incised through horizontally-bedded sedimentary strata, mainly

composed of dolomite and limestone with some shale and sandstone (Panfil and Jacobson, 2001;

Schumacher and Smith, 2018). In general, main channels and major tributaries of both

watersheds flow in deep and narrow valleys, with relatively high gradients, and in bedrock-

16

influenced riffle-pool streams with gravelly beds (Jacobson, 1995; Jacobson and Primm, 1997;

Skaer and Cook, 2005).

Geology and soils

Both watersheds drain in the Salem Plateau of the Ozark Highlands, which contain

Cambrian and Ordovician sedimentary rocks composed primarily of dolomites, chert, and

sandstones (Figure 3) (Adamski et al., 1995; USDA-NRCS, 2006). The Cambrian Eminence and

Potosi dolomites make up 74% of the surficial bedrock in Mineral Fork and Mill Creek

watersheds (Table 5). This formation was mineralized by hydrothermal fluid interaction along

orogenic belts during the Cambrian period and has been mined for shallow deposits of galena,

smithsonite (zinc carbonate ore), and barite (barium sulfate ore) since at least the early 1800s in

the Southeast Missouri Barite District in Washington County (Gregg and Shelton, 1989; Mugel,

2017).

Upland soils in Washington County, Missouri are generally formed in parent materials

consisting of a thin layer of silty Pleistocene loess over cherty clay residuum formed from the

weathering of the dolomites and limestones in the region (Skaer and Cook, 2005). The residuum

in the Ozarks is about 3 to 12 m thick, although locally it can be greater than 60 m (Seeger,

2006). Most of the uplands soils occur on gently-sloping to moderately-steep slopes with a

fragipan and gently-sloping to very-steep slopes containing chert fragments (Nigh and

Schroeder, 2002). In total, these watersheds contain 50.1 km2 of floodplain and alluvial terrace

soils with the Cedargap series occupying 70% of the floodplain soil area (Table 6). The

Haymond and Kaintuck series occur on larger floodplains, where the Cedargap and Bloomsdale

soils are commonly found on the valley floor of the narrow upstream reaches (Skaer and Cook,

17

2005). Upper stream bank deposits were formed by overbank deposition and are composed of

silt loam to fine sandy loam with >90% <2 mm sediment particles (Skaer and Cook, 2005).

Lower bank units were typically formed by bar and bench deposition (now stratigraphically

buried by overbank floodplain deposits) that are composed of coarser materials with loam to

sandy loam textures with <80% <2 mm including gravel- and cobble-sized fragments (Skaer and

Cook, 2005).

Climate and hydrology

Southeastern Missouri has a moist continental climate region (Peel et al., 2007; Skaer and

Cook, 2005). From 1990-2019, the mean monthly rainfall in Southeast Missouri ranged from

6.5- 13.7 cm with an average of 9.7 cm per month. The highest monthly rainfall totals (>10 cm)

occur in May, with typically less monthly precipitation (<9 cm) during the winter in December,

January, and February (MRCC, 2018). Snowfall occurs from November to March with totals

depths from 1.8 to 8.1 cm per month, with an average of 5.1 cm/month during the winter.

Between 1990 and 2019, the average annual temperature ranged from 12-15°C with an average

of 13°C. Over that period, average monthly temperatures range from -0.6°C in January to 25°C

in July (MRCC, 2018). Over the last 30 years, overall precipitation and temperature trends show

consistent, slightly increasing temperatures and overall rainfall since 1990 (MRCC, 2018).

Streamflow typically peaks in spring and rapidly declines through the summer. There are

no USGS gages located in the two watersheds. The mean annual discharge is 5.7 m3/s for

Mineral Fork and 1.6 m3/s for Mill Creek based on regional drainage area-discharge regression

equations developed from available USGS gaging data (Appendix A). The estimated maximum

annual discharge is 488 m3/s for Mineral Fork and 137 m3/s for Mill Creek. The uplands contain

18

karst features, and most low order stream channels are ephemeral or perennial “losing” streams

(USDA-NRCS, 2006). There are no natural lakes or ponds in the study area, however many

ponds have been constructed to trap mine tailings, support recreation, or supply water for

livestock purposes (Nigh and Schroeder, 2002).

Settlement and land use history

Historical land use. Oak-woodlands was the primary vegetation cover type in the pre-

settlement period in the study area with denser deciduous and pine forests occupying steep valley

slopes and bottoms (Nigh and Schroeder, 2002). These forests were logged and cleared to

varying extent across the Ozarks to support the settlement and economic growth of the region.

The second-growth forest was denser and with different composition compared to pre-settlement

conditions and was first harvested in the 1950s (Jacobson and Primm, 1997; Nigh and Schroeder,

2002).

The first phase of European settlement in the study area was by French miners in the

early to middle 1700s who worked shallow lead pits for galena around the towns of Potosi and

Old Lead Mines located in the Mineral Fork watershed (Mugel, 2017). The French mining

operations were abandoned after several years leaving only relatively small farming villages. The

second phase of European settlers began clearing the flatter uplands and valley floors for pasture

or row-crop agriculture around the 1840s (Jacobson and Primm, 1997). However, when the Civil

War ended and railroads extended lines into the region, farming activity increased after 1865

including more farm acreage, clearing and cultivation of hillslopes, and stripping the land for

mining purposes (Nigh and Schroeder, 2002). The resulting vegetation and soil disturbances

increased runoff and soil erosion rates significantly in many Ozark watersheds causing soil loss

19

and fertility problems, headwater stream incision, and accelerated delivery of gravel sediment to

main channels (Jacobson, 1995; Jacobson and Gran, 1999).

Many farmers would work or lease out shallow pit mines on their land during the winter

for galena and barite (locally known as “tiff”) in the 1800s. Then, more modern mining

operations moved into the district beginning in the early 1930s (Mugel, 2017). Surface soils

contained barite as residual deposits which were separated from the clayey host material by

processing in grinding and washer plants near Mineral Point (on Mill Creek) and northeast of

Potosi (along tributaries of Mineral Fork) (Mugel, 2017). The mining wastes were diverted into

tailings ponds within Mineral Fork and Mill Creek watersheds (Smith and Schumacher, 1993).

There are over 60 abandoned tailings ponds in the Barite District today storing a total of 39

million tons of tailings wastes (Mugel, 2017). Large tailings ponds and dams built between 1935-

1991 to trap mine tailings and eroding soil are distributed throughout the middle and lower

portions of these watersheds (Figure 4) (Mugel, 2017; MSDIS, 2019). There are 5.2 km2 of

ponds and a combination of 40 active wet and dry dams between the two watersheds. These

tailings dams range from 4 m to 31 m high with drainage areas ranging from 0.1 to 68.8 km2

(MSDIS, 2019). One of the largest ponds with a dam in the study area is Sunnen Lake in Mineral

Fork watershed which was developed for recreation and traps about one-half of the inflowing

sediment load (USGS, 2018a). Over 27% of the combined drainage area of the study watersheds

is located behind large tailings dams that are assumed to retain most of the runoff and trap all the

sediment flowing to them. Historically, there were probably more operating dams, but many

have filled in with sediment or were breached in recent time (MSDIS, 2019). Overall, about 12%

(80 km2) of the land area for these two watersheds was disturbed by surface barite mining

including pits, ponds and tailings dams (Schumacher and Smith, 2018). Approximately 1.8

20

million tons of barite were produced in the district until the last mine closed in 1998

(Schumacher and Smith, 2018).

Legacy over-bank deposits most likely occur along the floodplains of Mineral Fork and

Mill Creek below areas disturbed by cultivation, mining, and roadways. Field observations made

during this study indicate that buried A-horizons can be found up to one meter deep in the cut-

bank profiles suggesting that eroded soil was deposited on older floodplains since settlement

(Pavlowsky et al., 2017; Jordan, 2019). Tailings dams can create flow obstructions which can

trap sediment and increase the rate of legacy sediment deposition along streams (Trimble and

Lund, 1982; Walter and Merritts, 2008; Schenk and Hupp, 2009). For streams in smaller

watersheds, the higher banks formed by legacy deposits may produce deeper flows that can

generate higher stream powers and increase bank erosion rates (i.e. Knox, 1987; Lecce, 1997;

Ward et al., 2016).

Land use and land cover. Forestland is the major land use within these watersheds

based on the 2010-2017 National Agricultural Statistics Service (NASS) Crop Database (Table

7). Deciduous forest covered 79.3% of the watershed in 2017 (Figure 5). Today, wider valley

bottoms are usually cleared for agriculture (Nigh and Schroeder, 2002). Agricultural land

occupies 9.3% of the land area in the study, with pastureland covering 9% and 0.3% as cropland.

Cattle and poultry are the main types of livestock produced in Washington County (USDA,

2017). Cropland which includes row crops, double crops, small grains, and fallow ground only

covers about 0.1% of the area and alfalfa and other hay crops about 0.2% of the watershed

(USDA-NASS, 2018). The remainder of the watershed area is developed land (5.4%) or in

wetlands and open water (0.6%). Most of the urban area is formed in Potosi, Missouri

(population of 2,626 in 2017) which drain into both Mineral Fork and Mill Creek watersheds and

21

Mineral Point, Missouri (population of 354 in 2017) located east of Potosi, which drain into Mill

Creek (Figure 5) (US Census Bureau, 2017).

22

Tab

le 4

. 1

2-D

igit

HU

C w

ater

shed

s w

ith

in M

iner

al F

ork

an

d M

ill

Cre

ek.

*A

d =

dra

inag

e ar

ea

Wat

ersh

edA

d*

Ad B

elow

% A

d b

ehin

d

Wat

ersh

eds

Abbre

viat

ions

Typ

e(k

m2)

Dam

s (k

m2)

Dam

sU

rban

Agr

icul

ture

Fore

stM

ined

Mill

Cre

ekM

C12-D

igit

HU

C132.6

96.2

28

76

72

15

Min

eral

Fork

MF

12-D

igit

HU

C51.5

42.3

18

33

90

4

Cle

ar C

reek

-Min

eral

Fork

CC

MF

12-D

igit

HU

C98.8

75.6

24

34

92

2

Old

Min

es C

reek

OM

C12-D

igit

HU

C48.1

39.4

17

76

75

11

Min

e a

Bre

ton

Cre

ekM

BC

12-D

igit

HU

C123.6

105.4

15

815

73

4

Four

che

a R

enau

ltF

R12-D

igit

HU

C100.7

96.8

44

17

80

0

Sun

nen

Lak

e-F

our

che

a R

enau

ltS

LF

R12-D

igit

HU

C68.8

68.8

100

47

88

0

Min

eral

Fork

(W

hole

)M

F-W

hole

10-D

igit

HU

C490.5

428.6

27

510

82

3

Lan

d u

se (

%)

23

Table 5. Descriptions of bedrock geology in Mineral Fork and Mill Creek watersheds.

Unit Name Symbol Geologic Age Primary Rock

Type Secondary Rock

Type %

Area Eminence and Potosi dolomite Cep Cambrian Dolomite Chert 74 Gasconade dolomite Og Ordovician Dolomite Sandstone 21 Roubidoux sandstone and dolomite Or Ordovician Sandstone Chert, Dolomite 4 Elvins Bonne Terre Dolomite Ceb Cambrian Dolomite Conglomerate 1

Table 6. Alluvial soils within Mineral Fork and Mill Creek watersheds.

Soil Series Texture Landform Flood

Frequency Soil Order

Area

(km2) % of

Area Cedargap gravelly silt loam Floodplain

Frequently

Flooded Mollisols 34.83 69.7

Racket loam Floodplain Frequently


Razort silt loam Floodplain Occasionally

Flooded Alfisols 3.82 7.6

Bloomsdale silt loam Floodplain Frequently


Haymond silt loam Floodplain Frequently

Flooded Inceptisols 1.77 3.5

Higdon silt loam Stream terrace Occasionally


Sturkie silt loam Floodplain Occasionally


Kaintuck-Relfe

complex sandy loam Floodplain

Frequently

Flooded Entisols 0.62 1.2

Horsecreek silt loam Stream terrace Occasionally


Racoon-Freeburg

complex silt loam Stream terrace

Occasionally


Deible silt loam Stream terrace Rarely


24

Table 7. Change in land cover from 2010 to 2017 without mined land.

% of Land Cover 2010 2017 % Change

Forest 84.1 84.7 0.0

Pastureland 10.3 9.0 -12.3

Urban 5.0 5.4 4.3

Cropland 0.0 0.3 29.7

Water/Wetlands 0.7 0.6 -9.3

*(USDA-NASS, 2018)

25

Figure 2. Mineral Fork and Mill Creek watersheds within the Big River in relation to the Old

Lead Belt and the Barite Mining District.

26

Figure 3. Geology of Mineral Fork and Mill Creek.

Figure 4. Major mined areas in Mineral Fork and Mill Creek watersheds.

27

Figure 5. Land Use classification from USDA-NASS 2017 for Mineral Fork and Mill Creek

watersheds.

28

METHODS

This study assessed the volumetric changes of bank and bar landforms between 1995 and

2015 and then converted the volumes into masses of eroded and deposited fine sediment. The

masses of in-channel fine sediment erosion and storage were then compared with sediment

supplied by upland erosion and stream loads derived from STEPL modeling to develop a

sediment budget for the Mineral Fork and Mill Creek watersheds. The sediment budget was used

to assess the importance of bank and bar sediment processes and fine sediment load contributions

compared to total sediment transport for the watershed. The methods of the study are described

below including channel bank and bar assessment, spatial data sets and analysis, geomorphic

spatial analysis, STEPL sediment load modeling, and sediment budget framework.

Channel bank and bar assessment

Ozark streambanks are typically formed in floodplain deposits composed of two

sedimentary units, a finer-grained silty unit overlying a coarser-grained loamy unit containing

gravel (Panfil and Jacobson, 2001; Skaer and Cook, 2005; Owen et al., 2011). The upper unit

was formed by overbank flood deposition of suspended sediment composed of silt and clay with

lesser amounts of sand. The lower unit was formed by the deposition of bed-load along the

channel bed with finer sediments filling pore spaces (Panfil and Jacobson, 2001; Owen et al.,

2011). Profile descriptions of floodplain parent materials with varying texture in the study area

include Cedargap (gravelly), Kaintuck (sandy), and Haymond (silty) soil series (Skaer and Cook,

2005). In contrast, bar deposits are coarser than adjacent bank deposits and are generally

composed of sand and gravel (2-64 mm) with some cobble-sized clasts (64-256 mm) and finer

29

materials (<63 um) (Panfil and Jacobson, 2001; Pavlowsky et al., 2017). Bar forms are deposited

on the channel bed in zones of flow separation (e.g., point and delta bars) or where sediment

transport capacity is low relatively to sediment supply (e.g., center and side bars) (Rosgen,

1994). The profile characteristics of the Relfe soils generally describe the sedimentology of bar

features in the study area (Skaer and Cook, 2005).

As defined here, fine sediment is the material fraction of a bank or bar deposit less than

two millimeter in diameter including sand, silt, and clay particles. This fraction includes

sediment transported both in suspension (suspended load) and saltation or traction (bed-load).

Suspended sediment particles are assumed to be composed mostly of silt and clay particles (<63

µm) with some finer sand particles (<250 µm) (Rosgen, 1994). For example, sand percentages

(63-2,000 µm) in suspended sediment loads averaged from 6 to 39% in five southeastern

Minnesota rivers (Groten et al., 2016) and from 2 to 25% in Big River which receives flow from

both Mineral Fork and Mill Creek (Barr, 2016). In comparison, the sand content in floodplain

deposits in the study area varies from less than 20% in upper units to 10 to 40% in lower/coarser

units (Skaer and Cook, 2005). Thus, the fine sediment fraction evaluated for this study is

assumed to be similar in texture to that expected in the suspended load of these streams. The

percent fines were calculated by subtracting the % of coarse sediment (>2 mm) from 100%.

Channel and sediment assessment procedures. Field surveys of bank and bar location,

height, and stratigraphy were completed at 20 sites to provide data needed to verify bank height

measurements using LiDAR and estimate bank unit thickness based on local influences of stream

order and bank height (Appendix B). Sampling sites were distributed throughout the study

watersheds along tributaries and main channel at accessible locations not affected by road

crossings or local disturbances (Figure 6). GPS location and several photographs were collected

30

at each site. A stadia rod or folding ruler was used to measure bank height from the bank top

(i.e., near bank-full stage) to the bank toe. The bank toe was typically below the waterline at the

break in slope and texture, which was where the base of the floodplain bank meets the flatter

channel bed. Water depths were measured at the bank toe and channel thalweg (deepest point).

The cut-bank was scraped clean to identify stratigraphy including unit boundaries, sand or gravel

lenses, and buried soils.

Fourteen sediment samples were collected from upper bank (7) and lower bank (7)

sedimentary units at seven sites in Mineral Fork watershed to quantify the percentage of fine

sediment in the deposits (Figure 6; Appendix C). Composite samples from 0.2 to 0.5 m thick

were collected from cut-bank exposures by vertical scraping at a uniform depth. All sediment

samples were bagged and labeled in the field and returned to the laboratory at Missouri State

University for size analysis. The field samples were dried at 60°C in an oven, disaggregated with

a mortar and pestle, and passed through a 2 mm sieve. The fine sediment fraction reported as the

<2 mm mass divided by the total sample mass. The total field sample sometimes included

coarser clasts up to 64 mm in diameter.

Bank deposit and unit characteristics. Estimates of the thickness and fine sediment

content of upper and lower bank units were needed to apportion fine sediment fractions for

budget calculations. Analysis of stratigraphic measurements indicated that coarse unit thickness

averages about 55% of total bank height (as measured from the thalweg) across the range of

different bank heights evaluated for this study (Figure 7).

Field data and published information were used to develop relationships to predict the

fine sediment content of bank deposits. No trend in texture of the upper bank unit was indicated

for either bank height or stream order. Therefore, a constant value of a 90% fine sediment

31

fraction by volume (and 10% >2 mm) was assumed for all upper banks mapped as the Cedargap

soil series which included 70% of the floodplain soils in the study area (Skaer and Cook, 2005;

Figure 8; Appendix C). Floodplain banks associated with other soil series tend to have finer

upper units and were assumed to contain 100% fine sediment (Skaer and Cook, 2005). Sediment

samples from five of the seven sites plot along the 10% >2 mm line (90% fine sediment).

Further, this value also approximates the average composition of the upper A and B horizons of

the Cedargap soil series which represents the majority of sampled floodplains and previously

mapped soils along these streams (Skaer and Cook, 2005; Appendix B).

In contrast to the upper unit, the lower bank unit tends to become finer with increasing

bank height (Figure 8). Again, no trend was found with stream order, however, the sample size

was small. In the study area, banks with lower heights tend to be formed in geomorphic settings

associated with coarser sediment: (i) gravelly bench deposits where fine sediment is beginning to

bury coarse bar deposits to form young floodplains as shown by the Relfe soil series; and (ii)

gravelly floodplain deposits located along smaller and steeper channels where coarse sediment

transport and deposition is more frequent as shown by the Cedargap and Bloomsdale soils series

(Skaer and Cook, 2005). In contrast, higher banks tend occur in geomorphic settings associated

with finer-grained deposits: (i) floodplains along larger streams with lower slopes and wider

valley floors that deposit more silt and sand as shown by the Kaintuck and Haymond soil series;

and (ii) higher terraces along smaller streams as shown by the Higdon soil series (Skaer and

Cook, 2005). A linear regression equation was not appropriate for predicting textural

characteristics of lower bank units since deposits with similar textures were clustered according

to geomorphic features with discrete characteristics, not those grading into one another. Thus, a

step-function was used to classify lower unit texture according natural breaks with bank height as

32

follows: 40% fine sediment for <1.1 m height; 60% fine sediment for 1.1 m to 1.4 m height; and

70% fine sediment for >1.4 m height (Figure 8).

Bar Deposit Characteristics. No bar sediment samples were evaluated for this study.

Published values indicate that total pore or void space in gravel deposits generally averages

about 40%, thus comparing well with samples from the lower bank units composed of older

buried bar deposits (i.e., <40% fines by volume for low banks, Figure 8). However, fine

sediment does not typically fill in all the open spaces in recent or well-sorted gravel deposits.

Therefore, fine sediment content is typically less than the total open space might allow in bar

deposits ranging from 20 to 25% for silt and clay and up to 35% for sand (StormTech, 2012;

Dunning, 2017). Moreover, textural analyses of subsurface samples from the profile of the Relfe

soil series which occurs on larger bench and bar surfaces along Mineral Fork contains 20 to 30%

fine sediment (Skaer and Cook, 2005). Based on the evaluation above, it was assumed that all

bar deposits contained 25% fine sediment by volume for this study.

Bulk Density. Assumed bulk density values were used to convert volumetric

measurements into mass units for the sediment budget. For bank deposits, a bulk density of 1.4

Mg/m3 was used for fine sediment and 2.2 Mg/m3 for coarse material >2 mm (Bunte and Abt,

2001; Skaer and Cook, 2005). For bar deposits, a bulk density of 1.9 Mg/m3 was used for fine

sediment and 2.2 Mg/m3 for coarse sediment (Manger, 1963; Bunte and Abt, 2001; Pavlowsky et

al., 2017).

Spatial Datasets

Aerial photograph analysis. Historical aerial photographs from 1995 and 2015 were

used to assess channel width, bank location, and bar area to evaluate changes over a 20-year

33

period (Table 8). Pre-georeferenced USGS Digital Orthophoto Quarter Quads (DOQQ) were

retrieved from the Missouri Spatial Data Information Service for 1995 and 2015 (MSDIS, 2017).

The 1995 aerial photos have a spatial resolution of 1 m and were flown between March 1, 1995

and April 6, 1995. The 2015 aerial photos have a 0.15 m spatial resolution and were flown

between March 15, 2015 and April 17, 2015.

To account for rectification differences between the two sets of aerial photos, a mean

point-to-point error was calculated (Hughes et al., 2006). The point-to-point error is the

measured distance between known points on the two sets of photographs (Table 8). For this

study, 30 hard points were chosen in the study area, typically at building corners, and the 2015

color leaf-off was used as the reference photo (Table 8). Other studies have used between six and

30 points depending on the size of the watershed (Mount and Louis, 2005; Hughes et al., 2006;

Martin and Pavlowsky, 2011). UTM coordinates were assigned to each of the 1995 and 2015

points in ESRI’s ArcMap 10.7 and the distance between each set of points was calculated using

the distance formula. The distance between each set of points ranged from 0.98 m to 7.69 m with

a mean point-to-point distance of 2.76 m (n=30). This mean point-to-point error was later

incorporated into the next step of assessing erosion and deposition polygons to eliminate the area

inside the detection limit of error.

Erosion and deposition polygons. Both the wetted channel bank lines and bar features

were digitized from the 1995 and 2015 aerial photograph sets at a 1:1,000 scale in ArcGIS

(Figure 9a, b) (De Rose and Basher, 2011; Spiekermann et al., 2017). The aerial photographs

were used to digitize the active channel with the protocol to identify the stream banks until they

were not visible. Bar features were distinguished using the wetted channel boundaries as a guide

34

and an active channel layer was created by combining the two sets of features. These features

were converted to polygons and classified as either wetted channel or a bar in the attribute table.

Areas of bank erosion and deposition were identified by overlay analysis of the 1995 and

2015 active channel polygon layers. Bank erosion areas were identified by areas of the 2015

active channel beyond the 1995 active channel polygon using the erase tool in ArcGIS.

Deposition areas were identified as areas of the 1995 active channel outside of the 2015 active

channel polygon using the same tool. The same procedure was used to identify areas of erosion

and deposition of bar areas. Finally, the areas of all erosion and deposition polygons were

calculated in ArcGIS. In all, there were a four different polygon features produced from this

analysis: 1) bank erosion; 2) bank deposition; 3) bar erosion; and 4) bar deposition.

Error analysis. To account for the error associated with georeferencing, the mean point-

to-point error was incorporated into the erosion and deposition polygon analysis. A buffer using

half of the mean point-to-point error distance (1.38 m) was placed around the erosion and

deposition polygons (Figure 9c, d) (Mount and Louis, 2005; Hughes et al., 2006; Owen et al.,

2011). Areas from the bank erosion and deposition that overlapped the error buffer were

removed from the original polygons, creating erosion and deposition areas that were beyond the

error buffer accounting for rectification differences between the photo years (Figure 9c, d)

(Rhoades et al., 2009; Martin and Pavlowsky, 2011).

LiDAR analysis. A LiDAR derived DEM with one-meter horizontal and 0.185 m

vertical resolution was used to assign bank and bar heights to polygons and create a stream

network. The LiDAR derived DEM was obtained from MSDIS for Washington County and parts

of St. Francois County was flown June 30, 2011 (Table 8) (MSDIS, 2017). The LiDAR DEM

was used to delineate a stream network using the Strahler Stream Order method within each

35

watershed using the hydrology toolbox in ArcGIS (Strahler, 1957). The DEM was used to create

a flow accumulation and flow direction raster to establish a stream network with the stream link

tool. A threshold of 100,000 pixels (0.1 km2) was used for stream order classification. There was

a total of six stream orders created from using the Strahler method (Figure 10). The first and

second stream orders were not easy to identify because of low visibility in these heavily forested

watersheds. Therefore, only 3% of the first order and 24% of the second order streams were

digitized and later were not considered as part of the erosion and deposition analysis. However,

77% of third order streams were fully digitized. Third order streams remained in the cell

analysis, but the 23% unassessed stream length was addressed separately to determine the mass.

The LiDAR DEM was also used to assign landform heights to each polygon classified as

erosion or deposition for both the bars and banks (Notebaert et al., 2009; Rhoades et al., 2009;

De Rose and Basher, 2011; Kessler et al., 2012; Spiekermann et al., 2017). Because the aerial

photographs dates were different than the LiDAR flight date, banks and bars heights were

sampled using the LiDAR where both erosion and deposition occurred. Heights were only

sampled on erosion and deposition polygons below dams and on the third, fourth, fifth, and sixth

order streams. Polygons in third and fourth order streams were sampled every two kilometers,

and fifth and sixth order streams were sampled every 1 km because the stream length is smaller.

Of the 152 sites sampled, 10 (7%) had depositional bank heights larger than erosional bank

height. It was assumed that the cut-bank side of the channel should occur in the older part of the

floodplain which is higher due to a longer period of deposition. Therefore, the depositional bank

heights for these sites were corrected to equal those of the erosional bank heights. Of the 157

sites sampled, 21 (13%) had depositional bar heights larger than erosional bar heights.

36

To account for the elevation inaccuracies from water reflection in the LiDAR, the

assigned bank and bar heights were corrected to include water depths using field-based channel

topographic surveys. Bank height and water depth measurements were collected during rapid

field assessments that were completed throughout the watershed (Appendix B). The relationship

between bank heights recorded in the field and LiDAR banks heights shows an R2 value of

0.904, with the trend plotting just below the 1:1 as expected (Figure 11). This equation was used

to correct LiDAR height to actual field measured heights. In general, water depth added 0.07 to

0.14 m to LiDAR DEM derived bank heights. Average bank and bar heights were calculated for

each stream order in each 12-Digit HUC watershed.

Geomorphic spatial analysis at the reach-scale

Grid cell analysis. A longitudinal series of grid cells were overlain on digitized channel

centerlines to create a uniform reach scale for landform change analysis. Reach-scale studies of

stream geomorphology typically assess stream channel lengths that are 20-100 widths long

(Rosgen, 1994). For this study, active channel widths typically ranged from 10 m to 45 m.

Therefore, a cell length of 500 m was chosen for this study that is in the range of other studies of

Ozarks streams (Jacobson and Gran, 1999; Panfil and Jacobson, 2001; Pavlowsky et al., 2017).

These cells were created by placing a 100-meter buffer around the centerline derived from the

digitized stream network below dams that were then cut every 500 meters to create a total of 430

cells each 500 m long for the two study watersheds (Figure 12).

Cell analysis. The bank and bar erosion and deposition polygons were analyzed by the

cell unit as part of the reach-scale analysis in the third, fourth, fifth, and sixth order streams. In

ArcGIS, the “Intersect” tool is used to assign bank erosion, bank deposition, bar erosion, and bar

37

deposition polygons to each 500 m channel cell and the area of each was recalculated. If a

polygon was overlapping two cells, it would be divided into two polygons, one in each cell.

Finally, the average bank and bar heights for each of the cells were attributed by values from

each 12-Digit HUC watershed to each stream order. The bank and bar heights were multiplied by

the area to calculate the overall volume of sediment for each of the four different features. These

sediment volumes will ultimately be used in the sediment budget. (Table 9; Figure 13; Appendix

D-E). Results of cell locations and analyses are stored on the Ozarks Environmental and Water

Resources Institute (OEWRI) server. Lastly, unmeasured lengths, mainly in the third order

streams, were added to the masses from the cell analysis to the determine the volume of the

missing stream length in subwatershed. The volume of erosion/deposition for bank/bars in the

unassessed stream length was determined by taking the average volume of third order cells in per

12-Digit HUC subwatershed. The average cell volume (mass/0.5 km) was multiplied by the

length of unassessed stream order length below the dams to get the complete in-channel sediment

budget. The calculation and analysis of these values will be presented later in the results chapter.

Sediment budget development

The sediment budget approach applied in this study generally followed Trimble (1983)

and Trimble and Lund (1982). Sediment budgets measure the amount of sediment eroded and

stored in different landform units of a watershed (i.e. uplands, headwaters, floodplains, and in-

channel processes) over a period of time (Phillips, 1991; Beach, 1994; Trimble, 1999). To create

detailed sediment budgets, both sediment storage zones and active erosion zones need to be

added together to determine the output of sediment within a watershed (Davis, 2009). For

example, storage can occur in uplands at the base of slopes, on floodplains, in gravel bars, or in

38

impoundments (i.e. reservoirs, dams, lakes, ponds) (Trimble and Lund, 1982; Renwick et al.,

2005; Joyce et al., 2018). Additionally, sediment can be lost through sheet and rill erosion in the

uplands, re-mobilization of stored in-channel sediment (bars), or bank erosion (Trimble, 1999;

Davis, 2009; Lauer et al., 2017). Each of these factors will be incorporated into a sediment

budget using in-channel masses from this study, predicted sheet and rill erosion from uplands

and sediment loads from streams by STEPL modeling, and floodplain deposition rates based on

previous studies (Table 10).

STEPL Modeling. By using algorithms, Spreadsheet Tool for Estimating Pollutant

Loads (STEPL) calculates the nonpoint source loads, including fine sediment, nutrients, and

runoff, from the uplands of a watershed for predefined land use categories (urban, cropland,

pastureland, forest, and user-defined) (Tetra Tech, 2018). STEPL is a downloadable Microsoft

Excel spreadsheet that includes default parameters and options for users to customize and modify

inputs (WiDNR, 2014). The inputs for STEPL include: (1) land use area, (2) precipitation, (3)

agricultural animal numbers, (4) Universal Soil Loss Equation (USLE) output based on variable

Kf- and LS-factors, and (5) hydrologic soil group (Appendix F-G) (Tetra Tech, 2018). Much of

this data was obtained from the Soil Survey Geographic Database (SSURGO) and land-use data

from USDA-NASS (USDA-NRCS, 2017; USDA-NASS, 2018).

The User-Defined land use category was manipulated to represent areas within the

watershed that were mined. The 2017 land use data often classified the areas influenced by lead

or barite surface mining as forested (Figure 14). Forested lands typically have lower runoff and

sediment loads than agricultural land. Also, the mined lands within the watershed were more

representative of old construction sites that typically do not have as much vegetative cover and

bare ground is subject to increased runoff and soil erosion. Mined lands include features such as

39

surface mining pits and tailings piles, ponds/dams, and areas of soil disturbance that are

becoming forest covered. The area of mined land was mapped using the 2015 aerial photos and

2011 LiDAR dataset and used to reclassify the land use in Mineral Fork and Mill Creek (Figure

4, 14). The area of the watershed classified as mined lands was included in the User-Defined

category in STEPL.

The suspended sediment load in STEPL is computed based on the USLE and the

sediment delivery ratio (Park et al., 2014; 2015). STEPL is not a spatial model and it calculates

sediment loading for the watershed using default or generalized variables. Therefore, for this

study, STEPL was manipulated into being more spatially weighted by using specific soil series

data to derive area weighted K-, LS-, and C-Factors for each of the different land uses (Appendix

G) (USDA-NRCS, 2017). Finally, the total suspended sediment load is calculated by multiplying

soil erosion by the sediment delivery ratio, which is a rough estimate of sediment deposition and

storage within the watershed (Tetra Tech, 2018). The sediment delivery ratio (SDR) is calculated

based on the watershed area where a lower percentage of eroded soil is exported out of the

watershed as the drainage area increases (NRCS, 1983; James, 2013). Therefore, the sediment

load from STEPL represents the total mass of sediment leaving the watershed from sheet and rill

erosion annually after the SDR is applied to the upland erosion mass.

Tailings dam influences. Mineral Fork and Mill Creek watersheds contain 40 large

tailings dams and recreational lake dams along tributary and headwater streams according to the

records in the Missouri 2019 Dams shapefile (MSDIS, 2019) (Appendix H). The largest dams

that were capable of trapping 100% of the fine sediment loads were identified from published

locations and dam heights (MSDIS, 2019) and observations of disconnected drainage systems

from LiDAR (collected 2011) and aerial photography (collected 2015). A secondary “below

40

dam” drainage divide was delineated through the location points of most downstream large dams

along the tributary network to delineate the effective sediment-contributing drainage area for

each watershed. The following “below dam” drainage area was reduced by 27% for Mineral

Fork and 28% for Mill Creek (Figure 12). It was assumed for sediment load modeling purposes

that all the tailings dams trapped 100% of the sediment. However, based on trap-efficiency

equations, the Sunnen Lake dam passes about 50% of the suspended sediment load it receives

annually (St. Louis District Corps of Engineers, 1970; Ward et al., 2016).

STEPL was used to calculate the percent of the sediment load that was reduced due to

runoff retention and sediment deposition in the old tailing’s ponds and Sunnen Lake. First,

STEPL was used to estimate the upland erosion and stream loads for the entire watershed area

including the drainage areas behind the dams. Next, STEPL was applied only to the land areas

below the most downstream dam on a tributary, not including land areas above the dam. The

total load and below dam load were compared to determine the percent reduction in the overall

sediment load from the effects of dams. The drainage area above Sunnen Lake dam was assessed

separately to estimate suspended sediment load at the dam and then reduce by a best

management practice (BMP) efficiency setting of 50%. The reduced stream load from the

Sunnen Lake outlet was added to the upland erosion load for the “below dam” drainage area for

sediment budget calculations for the whole Mineral Fork watershed.

Overbank floodplain deposition. Overbank sedimentation storage was estimated using

deposition rates from research near Mineral Fork and Mill Creek and a review of published

results (Table 1). Based on the soil maps, Mineral Fork has 25.1 km2 of frequently flooded soils

and 3.9 km2 of occasionally flooded soils. Mill Creek has a total area of 5.4 km2 of frequently

flooded soils and 0.7 km2 of occasionally flooded soils mapped in the watershed (Skaer and

41

Cook, 2005). A review of floodplain sedimentation rates derived from Big River floodplain core

profiles using Cs-137 to identify the 1963 bomb testing peak showed that while higher

deposition rates >10 mm/yr occur on lower “in-channel” floodplain and bench surfaces, more

moderate rates from 6 and 10 mm/yr occur on floodplain surfaces at/near bank-full stage.

However, lower rates from 1-3 mm/yr occur on higher floodplains in wider valleys in stable

riparian zones (Pavlowsky, 2013; Keppel et al., 2015; Pavlowsky and Owen, 2015; Jordan,

2019). In a review of the literature, streams with drainage areas and soil conditions similar to the

study area tend to have lower floodplain sedimentation rates (1-10 mm/yr) (Owen et al., 2011;

Keppel et al., 2015; Pavlowsky and Owen, 2015). From the review and field observations, it was

assumed that soils frequently flooded had a deposition rate of 3 mm/yr and occasionally flooded

soils had a rate of 0.5 mm/yr In order to calculate mass, the total deposition volume (area times

deposition rate) was multiplied by 1.4 Mg/m3 (Manger, 1963; Pavlowsky et al., 2017).

42

Table 8. Aerial photograph characteristics.

Year Source Flight Date Type Resolution

(m)

Point to

Point Range

(m)

Mean Point

to Point

Error (m)

Buffer

(m)

2015 MSDIS 3/15/2015 True color leaf-off

DOQQ 0.15 Reference Image

1995 MSDIS 4/6/1995 Black and White

DOQQ 1 0.98 - 7.69 2.76 1.38

2011 MSDIS 6/30/2011 LiDAR DEM 1 N/A N/A N/A

Table 9. Definition of variables for deposit volume and mass calculations.

Variable Equation Bank Erosion and Deposition Cells Average Width (m) *Area (m2) / *Length (m) Lateral Change Rate (m/yr) Average Width (m) / 20 (yr) Total Volume (m3) *Area (m2) * *Bank Height (m) Lower Unit Volume (m3) (Total Volume * 0.55) * Fraction of Fines (0.4 - 0.7) Upper Unit Volume (m3) (Total Volume * 0.45) * Fraction of Fines (0.9 -1.0) Total Volume of Fines (m3) Lower Unit Volume (m3) + Upper Unit Volume (m3) Mass (Mg) Total Volume of Fines (m3) * bulk density (1.4 Mg/m3)

Bar Erosion and Deposition Cells Average Width (m) *Area (m2) / *Length (m) Total Volume (m3) *Area (m2) * *Bar Height (m) Total Volume of Fines (m3) Total Volume (m3) * Fraction of Fines (0.25) Mass (Mg) Total Volume of Fines (m3) * bulk density (1.9 Mg/m3) *Values from sampled LiDAR heights by subwatershed/stream order

43

Table 10. Description of sediment budget terms.

Component*# Description

Upland Erosion Overall soil erosion rates predicted by STEPL using variables in

appendix (Tetra Tech, 2018).

Floodplain Storage Estimated mass of sediment deposited into long-term storage on

frequently (3 mm/yr) and occasionally (0.5 mm/yr) flooded soil series

(Skaer and Cook, 2005). Annual deposition rates were based on

assumptions from literature review and limited regional data (Table 1).

Other Storage Upland Erosion rate (#1) minus floodplain (#2), bank, and bar

depositional storage rate and export load.

Bank Erosion (net) Sum of annual bank erosion and bank deposition rates (Figure 25).

Positive value indicates a net supply or release to the channel and

negative value indicates a net sink or storage from channel transport. Part

of the in-channel derived load (Table 25).

Bar Erosion (net) Sum of annual bar erosion and bar deposition rates (Figure 25). Positive

value indicates a net supply or release to the channel and negative value

indicates a net sink or storage from channel transport. Part of the in-

channel derived load (Table 25).

Upland Load Output of stream sediment from the watershed predicted by STEPL from

upland erosion (#1) after application of sediment delivery ratio (Tetra

Tech, 2018).

In-channel load Output of stream sediment from the watershed calculated by this study by

assessment of annual erosion and deposition rates of bank and bar

deposits (Table 25).

Export Load Total sediment load exported from the watershed outlet as the sum of

both upland (#6) and in-channel (#7) loads. The export load from the

Mineral Fork and Mill Creek watersheds would be assumed to enter Big

River (Table 27).

Sediment Yield Export load reported as a per unit area (km2) rate that indicates the

intensity of sediment production from the watershed (Table 27). *all units in Mg/yr except for sediment yield which is Mg/km2/yr #Positive (+) mass values denote erosion or the release of sediment to the channel, while negative

(-) values denote deposition or storage of sediment in colluvial or alluvial deposit

44

Figure 6. Location of field assessment sites.

45

Figure 7. Coarse unit thickness in bank deposits.

Figure 8. Texture of bank deposits.

46

Figure 9. Application of error to active channel features. (A) 2015 digitized active channel

compared to the (B) 1995 digitized active channel from the aerial photographs. (C) Areas of

erosion where parts of the active channel that do not overlap the 1995 active channel buffer (1.4

m). (D) Areas of deposition where parts of the active channel that do not overlap the 2015 active

channel buffer (1.4 m).

2015 1995

1995 Buffer 2015 Buffer

Erosion Deposition

47

Figure 10. Digitized and delineated stream network

.

Figure 11. Comparison of LiDAR bank height to field bank height.

48

Figure 12. Cell distribution below dams by stream order.

49

Figure 13. Deposit volume to fine sediment mass conversion by cell.

Bank Volume

Total Cell Volume

Lower Unit

55%

Volume of Fines

40% L, 60% M, 70% H

Bulk Density

1.4 Mg/m3

Mass (Mg)

Lower Unit

Bank Mass (Mg)

Lower + Upper Unit

Channel Mass (Mg)

Bank + Bar

Upper Unit

45%

Volume of Fines

Cedargap 90%, Other 100%

Bulk Density

1.4 Mg/m3

Mass (Mg)

Upper Unit

Bar Volume

Total Cell Volume

Volume of Fines

25%

Bulk Density

1.9 Mg/m3

Mass (Mg)

Bar Mass of Fines

50

Figure 14. Mining areas classified as forest from the 2015 DOQQ aerial photo.

51

RESULTS AND DISCUSSION

Channel delineation and network analysis

Stream network and orders. The stream networks were delineated for each watershed

from the LiDAR data using the Strahler Order method. The total channel length by stream order

for Mineral Fork was as follows: 486 km, first; 224 km, second; 104 km, third; 51 km, fourth; 25

km, fifth; and 28 km, sixth (Table 11). Not all segments of the channel network could be

digitized into channel and bar features on the aerial photographs due to the resolution errors and

obstruction by trees and shadows. Only 4% of the first order and 29% of the second order

streams were digitized in the Mineral Fork watershed. Therefore, only the third, fourth, fifth, and

sixth stream orders were evaluated in this study. In Mineral Fork, 100% of the fourth, fifth, and

sixth order and 78% of the third order delineated streams were digitized (Table 11). Of the total

assessed stream length (188 km), 16% of the network length was above dams as follows: 18%,

third; 21%, fourth; 13%, fifth; and 0%, sixth. Since it was assumed that 100% of sediment load

was trapped behind the large tailing’s dams, the stream lengths above dams were not included in

the channel assessment, with the exception of Sunnen Lake dam with its 50% trap efficiency for

sediment. Therefore, the total assessed length by stream order in Mineral Fork was as follows: 86

km, third; 40 km, fourth; 22 km, fifth; and 28 km, sixth (Table 11).

The total channel length by stream order for Mill Creek was as follows: 139 km, first; 70

km, second; 31 km, third; 24 km, fourth; and 5 km, fifth (Table 12). The first order streams were

not visible and only 4% of the second order streams were digitized. Therefore, similar to Mineral

Fork, the in-channel analysis only included third, fourth, and fifth order streams in the Mill

Creek watershed. The digitized stream network included 100% of the fourth and fifth order and

73% of the third order stream lengths in the Mill Creek watershed. Dams were only located on

52

third order streams, leaving 20% of the stream length above dams. Therefore, the total assessed

length by stream order in Mill Creek was as follows: 25 km, third; 24 km, fourth; 5 km, fifth

(Table 12).

Cell distribution. The channel morphology in each watershed below dams was

compared between the two aerial photograph years to support the analysis of in-channel

contributions to sediment budgets. The digitized stream network was divided into 500-m long

channel cells to quantify the spatial patterns of bank and bar erosion and deposition areas in

stream order segments (Jacobson and Gran, 1999; Panfil and Jacobson, 2001). Mineral Fork had

344 cells within its watershed below dams. The cells in Mineral Fork were grouped by stream

order as follows: 44%, third; 28%, fourth; 13%, fifth; and 16%, sixth (Figure 15). Mill Creek had

86 cells. The cells in Mill Creek were distributed by order as follows: 38%, third; 50%, fourth;

and 12%, fifth (Figure 15). The 430 cells were used as the unit of assessment to sum net erosion

or deposition in the channel erosion and deposition areas and were multiplied by average

landform height values for each stream order in each 12-Digit HUC subwatersheds. Volume of

bank and bar landform changes were then summed to assess sediment erosion and deposition of

both the cell and stream order scales.

Bank and bar deposit assessment

The total gravel bar area was digitized in the 1995 and 2015 aerial photographs, while the

active channel was used to determine if the active channel was widening or laterally moving in a

cut-bank point to bar formation (Kondolf, 1997). Examples of the spatial distribution of erosion

and deposition using polygons are shown in Figures 16 and 17. Typically, both erosion and

deposition for banks and bars were observed in spatially similar locations (Joyce et al., 2018).

53

More specifically, where bank erosion occurred deposition impacts were adjacent to it. Figures

16 and 17 also showed a detailed map of multiple cells/reaches where there were erosion zones

that contributed the most to the sediment load. Similarly, gravel bars were present in all of the

reaches (cells) in the figures for 1995 and 2015 (Figures 16, 17). Additionally, the reaches were

used to determine if there was movement of the gravel bars downstream (Panfil and Jacobson,

2001).

After identifying all of the erosion and deposition polygons, it was determined how much

of the stream length was disturbed. The total length of bank erosion (i.e. cut-banks) in Mineral

Fork was 87.0 km in the third through sixth order streams, or 21% of the digitized stream length.

The total length of polygons defined as bank deposition in Mineral Fork was 78.6 km, or 19% of

the active channel length evaluated for this study. Similarly, in Mill Creek, the total length of

cut-banks was 23.6 km, or 24% of the digitized stream length. The total length of bank

deposition was 9.7 km or 10% of the digitized stream length. The frequencies of eroding channel

lengths observed in Mineral Fork and Mill Creek are similar to other Ozark streams where 20 to

40% of channel lengths are in disturbed active zones along the main channel segments (Martin

and Pavlowsky, 2011; Owen et al., 2011).

Variable discharge effects on planform analysis. Studies have shown that there are

errors associated with using aerial photographs to determine channel morphology (Mount and

Louis, 2005; De Rose and Basher, 2011). One of the disadvantages are rectification procedures

and the ability to consistently locate bank features between dates of photography such as cases

where the resolution of the image is low or the study area is in a dense woody riparian cover (De

Rose and Basher, 2011; Spiekermann et al., 2017). However, mean point-to-point errors and

other polynomial transformations from georeferencing can reduce inaccuracies by applying

54

buffers to remove areas that are inside of the limit of error (2 m to 5 m) (Mount and Louis, 2005;

Hughes et al., 2006; De Rose and Basher, 2011). This study used a mean point-to-point error of

2.8 m, and applied half of the error on each side of the stream creating a buffer of 1.4 m.

Another problem with using the aerial photographs was the need to check if the discharge

during the photograph dates were similar thus allowing channel morphology, and not water

depth, to describe wetted width dimensions. This is usually addressed by finding the flow

measurements from historical USGS gage records among photograph dates (Barr, 2016).

However, these small watersheds do not contain gaging stations. Further, the photographs used

from MSDIS did not have exact flight dates of when the photographs were taken, only a range of

dates. The closest gages to Mineral Fork and Mill Creek were south (upstream) of the watersheds

on the Big River at Richwoods (#7018100) and north (downstream) of the watersheds on the

Meramec River near Sullivan (#7014500) (USGS, 2018b). The antecedent flooding was

compared by assessing the peak annual flood discharge in the 5-year period before each aerial

photograph year (Figure 18). The period from 1990 to 1994 had higher annual floods compared

to 2010-2014 (Table 13). The average of the annual flood peak record was the mean annual flood

with a recurrence interval of 2.33 years. The average flood peak in the five years before aerial

photographs were collected was 1.5-1.7 times larger in 1995 compared to 2015. Therefore, the

flood power could have had an influence on the wider channel in 1995 and photograph series

taken after the period of more floods might yield sharper and wider banks and brighter and easier

to delineate bars.

Water surface width on the day of the aerial photographs were taken varied with baseflow

or recent runoff. In order to detect if there was an impact of channel flooding and water levels on

the active channel width, 14 different 500 m reaches in the third through sixth stream orders

55

were compared to determine if there was a significant difference in the active channel width

between the photograph years (Table 14). Based on a 1:1 line for the 1995 active channel widths

to the 2015 widths, there was an R2 value of 0.84 (Figure 19). Trends showed that when the

active channel width increased, 1995 had a wider channel than 2015. Alternatively, when the

active channel width decreased, 2015 had a wider channel than 1995 (Figure 19). Therefore, the

1:1 showed that there was not a significant difference between the different years. The average

difference in channel width (1.1 m) was less than half of the mean point-to-point error (2.8 m).

Further, there was relatively little scatter among the site pairs suggesting that the discharge and

depth/width relation was similar for both years and that bank and bar lines would not vary

significantly due to water depth errors.

Bank and bar heights as a factor for volume. Because the aerial photos dates were

different than the LiDAR flight date, a subsample of bank (n = 152) and bar (n=157) heights

were collected in stream orders (3rd – 6th) below the dams. The water reflection from the LiDAR

was corrected on these heights to include water depths using field-based channel topographic

surveys. Of the 152 bank sites sampled, 10 (7%) had depositional bank heights larger than

erosional bank height. It was assumed that the cut-bank side of the channel should typically

erode into the older formation of floodplain deposits (i.e. low terrace or historical floodplain),

which are assumed to be higher due to a longer period of deposition. Therefore, the depositional

bank heights for these sites were corrected to equal those of the erosional bank height. Similarly,

of the 157 bar sites sampled, only 21 (13%) had depositional bar heights larger than erosional bar

heights. An average height was calculated for each stream order for bank and bar erosion and

deposition for each 12-Digit HUC watershed (Table 15-18). The average bank and bar heights

were assigned to each cell based on stream order location and subwatershed. Lastly, unmeasured

56

lengths of the third order streams were added in the analysis to estimate the volumes of the

missing stream length for each subwatershed. The volumes of erosion/deposition for bank/bars

was determined by taking the average volume in third order cells in each subwatershed and

multiplied that number by the length of unassessed stream order length below the dams.

Mass of bank and bar erosion and deposition of fine sediment

Geomorphic trends. As expected, average bank and bar heights increase from third to

sixth order streams by 1.6 to 1.9 times for banks and 1.4 to 1.6 times for bars (Tables 15-18;

Figure 20). Third order banks averaged from 1.6 to 2.0 m high and sixth order banks from 2.7 to

2.8 m (Table 15). Average depositional bank heights were about one-third lower than eroding

banks (Table 6; Figure 20). Erosional and depositional bar heights tended to be within 10% of

one another with eroding bars usually higher, ranging overall from 1 to 1.4 m in third order

channels to 1.7 to 1.8 m in sixth order channels (Table 17-18; Figure 20). Except for sixth order

streams, average bar heights tended to be slightly higher than depositional banks (Figure 20).

While this trend may reflect variations in bank and bar heights downstream, and not within the

same reach, it does suggest that in this study the depositional banks are forming on lower bar

surfaces as young benches or shelves (Owen et al. 2011). Further, bar features in the Ozarks can

accrete to relatively high elevations near bank-full stage in disturbance reaches (Panfil and

Jacobson, 2001; Martin and Pavlowsky, 2011). Specifically, for the subwatersheds, both

erosional and depositional bank heights, and bar heights to a lesser degree, tended to be higher in

MC and MF subwatersheds which drained directly into the Big River (Figure 12). This trend is

expected since longitudinal bank lines and channel beds would grade to meet those of the larger

57

river with local base-level control and decreasing slopes increasing floodplain and channel

deposition rates.

As expected, active channel width (including the wetted channel bed and gravel bars)

increased downstream from about 8 to 10 m in third order streams to 40 to 55 m in sixth order

streams (Figure 21). Average channel widths both increased and decreased among stream orders

and subwatersheds over the 20-year study period (Figure 21). The largest increase by almost

40% occurred in third order streams in OMC subwatershed. This geomorphic response may have

been caused by recent land disturbances that increase runoff rates into relatively unstable

channels due to the presence of mobile gravel deposits possibly linked to the effects of

settlement pressure and lead and barite mining since the mid-1700s (Adamski et al., 1995;

Jacobson and Primm, 1997; Jacobson and Gran, 1999; Olson, 2017).

The largest decreases in width from 24 to 37% occurred in the SLFR subwatershed for

third to fifth order streams (no sixth order streams were mapped in SLFR) (Figure 22a). It is

possible that the drainage network was affected by the relatively larger floods prior to 1995 or

that more conservation practices for riparian buffers were implemented there since 1995

compared to the other watersheds (Jacobson and Pugh, 1997; Zaimes and Schultz, 2015). Higher

antecedent flood magnitudes preceding the collection of the 1995 photographs would suggest

that channel widths would at least be temporarily wider than average width in 1995 since bank

scour and vegetation removal would be expected to occur during larger floods (Table 13)

(Hagstrom et al., 2018). If this was the case, then channels would be expected to recover and be

less scoured during 2015. Thus, a tendency for decreased channel widths in 2015 might be

assumed given no other changes in land use or flood climatology. However, average width

differences were less than 10-20% with nine subwatershed-order classes indicating increases in

58

width and ten classes showing decreases in width over the 20-year period (Figure 22a). The

average annual erosion rate for the all the subwatersheds combined was 0.15 m/yr. This 50:50

distribution of width change suggests the higher antecedent flood frequency and magnitude did

not influence the results of the present study to a significant degree.

Bank erosion rates can be used to evaluate channel activity since relatively high rates

indicate unstable planform conditions with poorly organized bar forms in Ozark streams

(Jacobson, 1995; Jacobson and Primm, 1997; Martin and Pavlowsky, 2011). Bank erosion rates

>1-2 m/yr in smaller streams like those in this study were considered excessive (Harden et al.,

2009; Rhoades et al., 2009; De Rose and Basher, 2011; Kessler et al., 2013; Janes et al., 2017;

Spiekermann et al., 2017). In this study, average bank erosion rates and their range among the

subwatersheds increased with stream order as follows: third, 0.09 m/yr (0.04-0.10 m/yr); fourth,

0.13 m/yr (0.04-0.18 m/yr); fifth, 0.18 m/yr (0.05-0.28 m/yr); and sixth, 0.18 m/yr (0.27-0.43

m/yr). Average annual bank erosion rates as a percent of active channel width ranged from 0.2 to

1.2% from 1995 to 2015 for the seven subwatersheds evaluated for this study (Figure 22b).

There was a tendency for relatively higher rates in third and fourth order streams (>0.9%) and

lower rates in fifth and sixth order streams (<0.7%). Locally, one sixth order stream cell in MF

had an average bank erosion rate of 0.43 m/yr which was 8% of the active channel width. Bank

erosion rates for Mineral Fork and Mill Creek compare well with those reported for 40 stream

segments draining the Mid-Atlantic Piedmont with average rates from 0.4-0.19 m/yr and relative

rates averaging 2.5%, with a range of 0.9-4.4%, of the active channel width (Donovan et al.

2015).

Gravel bars are common in Ozarks streams and tend to cluster in disturbance zones

separated by relatively stable segments (Jacobson and Primm, 1997; Martin and Pavlowsky,

59

2011; Olson, 2017). In the Mineral Fork watershed, 1.39 km2 of bars were mapped in 1995 and

1.37 km2 in 2015 (1.4% decrease). On average, gravel bars in third and fourth order streams in

the Mineral Fork watershed covered 17 to 36% of the active channel area in 1995 and 31 to 43%

in 2015. Bar area in fifth and sixth order streams covered 29% of the active channel during both

1995 and 2015. In the Mill Creek watershed, 0.25 km2 of bars were mapped in 1995 and 0.27

km2 in 2015 (8% increase). The Mill Creek watershed had relatively higher bar areas in the

active channel compared to Mineral Fork overall with 29% in 1995 and 43% in 2015. Like the

active width, average bar width also tends to increase downstream from less than 5 m in third

order channels to 10-15 m in sixth order streams (Figure 23).

All stream order classes contained at least two subwatersheds where bar area in the active

channel increased by > 40% from 1995 to 2015 (Figure 24). The greatest increases in bar area

occurred in fifth order streams (up to 65% in FR). It is possible that some of the gravel sediment

previously released by historical or more recent land use disturbances and channel incision was

first deposited in in upstream reaches and then migrated downstream into higher order channels

in a wave-like process (Jacobson and Primm, 1997; Jacobson and Gran, 1999). Bar areas

decreased by 10-30% in third and fourth order channels in some subwatersheds perhaps as the

result of erosion and downstream transport of gravel (Figure 24). Surprisingly, bar areas in sixth

order streams of the lower Mineral Fork watershed did not change much between the two

photograph years (<10% increase) (Figure 24). Valley confinement by higher bluffs and lower

rates of lateral channel migration along the lower segments may have limited available bar

accommodation space along the lower Mineral Fork Creek (Lecce, 1997; De Rose and Basher,

2011; Janes et al., 2017).

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Patterns of bank and bar storage and erosion masses. The cell-level channel

characteristics and sediment samples collected in the field were used to estimate the total mass of

fines for bank and bar erosion and deposition. The alternation of depositing and eroding cells has

persisted in many Ozark streams since at least since the early 1900s (Jacobson, 1995). This can

also be an indication of a dynamic equilibrium condition among discharge, sediment supply, and

topography (Martin and Pavlowsky, 2011). Therefore, patterns of the bank and bar storage and

erosion were identified by subwatershed and stream order to determine the overall mass of

storage and erosion per cell to make sure to include these reach-scale sediment variations.

Sediment masses by cell were also used to determine areas where most of the bank erosion, bar

erosion, and net erosion load were located to identify which cells combined to make up 25% of

the overall load contribution of fine sediment.

Bank erosion. Of the 430, 500 m long cells assessed for this study, 98% produced bank

erosion and 93% bank deposition. As expected, average eroded masses increased from third to

sixth order streams by 11.8 to 15.5 times (Tables 19-20). Third order bank erosion masses per

cell averaged from 24 to 55 Mg/yr and sixth order from 357 to 821 Mg/yr (Table 19). Average

depositional bank masses were lower than eroding banks with rates from 11 to 40 Mg/yr in third

order and 198 to 328 Mg/yr in sixth order (Table 20).

The Mill Creek watershed had a total of 8,334 Mg/yr of sediment from bank erosion and

2,114 Mg/yr from bank deposition with a net supply of sediment to the channel system by bank

erosion of 6,190 Mg/yr (Table 21; Figure 25). Therefore, eroding banks were releasing nearly

four times as much fine sediment as they were depositing during the formation of new bench and

floodplain landforms. Only one of the 86 cells in Mill Creek did not contain bank erosion (Figure

26). Specifically, the locations in Mill Creek with the highest erosion (i.e. supplying 25% of the

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total bank erosion load, not including deposition) were in four cells along the main stem of Mill

Creek with inputs ranging from 353 to 739 Mg/yr in the per cell (Figure 26).

The Mineral Fork watershed had a total of 48,382 Mg/yr of bank erosion and 26,593

Mg/yr from bank deposition yielding a net sediment output from bank erosion of 21,789 Mg/yr

(Table 21). The subwatersheds within Mineral Fork indicated that MF and CCMF have the

highest contributions of sediment from bank erosion and deposition (Figure 25). These

watersheds contain the highest banks along main stem of Mineral Fork Creek which flows

directly into Big River. Moreover, MF supplies 46% of the overall bank erosion load and 34% of

the annual bank storage and CCMF contributes to 26% of bank erosion and 26% to bank

deposition to the Mineral Fork watershed. OMC and SLFR (which had 50% sediment trap from

Sunnen Lake) contain the smallest contributions to bank erosion and deposition loads. There was

no bank erosion measured in only seven cells in the following watersheds: OMC (1 cell), MBC

(3 cells), FR (2 cells), and SLFR (2 cells). Of the 344 cells in the whole Mineral Fork watershed,

the largest contributors of bank eroded sediment include five cells in MF and two in CCMF

(Figure 26). Ultimately, cells with the highest bank erosion rates were downstream from the

barite mined areas (Schumacher and Smith, 2018). The cells that accounted for 25% of the bank

erosion load ranged from 1,314 to 3,015 Mg/yr in the Mineral Fork watershed.

Bar erosion. Of the 430 cells, 90% included bar erosion and 91% bar deposition. Similar

to banks, average bar masses increase from third to sixth order streams by about 9.5 to 19.4 times

(Tables 22-23). Third order bar erosion masses per cell averaged from 11 to 82 Mg/yr and sixth

order from 401 to 440 Mg/yr (Table 22). Average depositional bar sediment rates were lower

than measured in eroding bars from 17 to 30 Mg/yr in third order streams but were higher than

bar erosion rates in the sixth order streams ranging from 391 to 593 Mg/yr (Table 23).

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The streams in Mill Creek and Mineral Fork watersheds flow through alluvial soils with

gravelly silt loam and channels with coarse beds. As expected, bars had a significant influence

on the fine sediment storage within each watershed. The Mill Creek watershed had a total of

5,546 Mg/yr of fine sediment released by bar erosion and 9,985 Mg/yr of storage by bar

deposition (Table 21). This demonstrated that the bar storage was 1.8 times the load of fine

sediment released by bar erosion. Even though bank erosion and deposition were a common

process in the Mill Creek watershed, gravel bars had higher rates of both erosion and storage

compared to banks (Figure 25). If bar erosion is the only factor being assessed (without bar

storage), Mill Creek had two cells that make up the highest erosion and 13 cells with no

measured bar erosion. The two cells that included 25% of the bar erosion load contributed 575

and 858 Mg/yr. The stream reaches affected by high erosion were again located on the main stem

of Mill Creek, compared to the cells indicating no erosion that were located in the lower-order

tributaries (Figure 27).

The Mineral Fork watershed had a total of 48,777 Mg/yr of bar erosion and 48,818 Mg/yr

from bar deposition (Table 21). Bars in Mineral Fork stored nearly equal amounts of fine

sediment by bar deposition as released by bar erosion (-41 Mg/yr of net storage). Recall that bar

area only decreased by -1.4% since 1995. Of the subwatersheds in Mineral Fork, the largest

influence of bar storage and bar erosion was in MF, CCMF, and MBC, with OMC and SLFR

having the lowest contribution of bar sediment (Table 21). MF and FR were the only two

subwatersheds that had more sediment put into bar storage than released by bar erosion (Figure

25). Subwatersheds CCMF, OMC, MBC, and SLFR had the highest annual bar erosion masses.

With bar deposition was not considered, the highest sediment masses released to the channel

from bar erosion were also located in MF (3 cells), CCMF (3 cells), and MBC (4 cells) (Figure

63

27). The cells that contributed to 25% of the bar erosion load ranged from 572 to 1,574 Mg/yr.

Again, deposition cells were located near reaches with high bar erosion rates and occurred

downstream from the barite mined areas (Figure 27). Of the 344 cells in the whole Mineral Fork

watershed, there were only a few without measured bar erosion cells in all of the subwatersheds:

MF (5 cells), CCMF (13 cells), OMC (4 cells), MBC (3 cells), FR (8 cells), and SLFR (2 cells).

Net Mass. When all factors were being included (bank and bar erosion and deposition),

Mill Creek had a net in-channel load of 1,751 Mg/yr (Table 21). Mill Creek had 59% of its cells

with a net erosion and 41% indicating either no erosion, in balance, or with a net storage.

Therefore, if stabilization practices were being considered for the Mill Creek watershed, they

would be most effective within the two high erosion cells near Fountain Farm Branch and in the

mining disturbed areas (Figure 28). The two cells that contributed to 25% of the overall net

erosion output yielded 519 and 841 Mg/yr of fine sediment. In Mill Creek there were more cells

releasing fine sediment than there were cells storing fine sediment (Figure 28, 29). However,

there was higher rates of net deposition occurred in the downstream segment of Mill Creek

(Figure 29).

Bank and bar erosion and deposition in Mineral Fork yielded a net in-channel sediment

load of 21,748 Mg/yr (Table 21). Moreover, 94% of the cells indicated net erosion, with 6%

having no erosion or net storage. On average the net sediment masses released per cell increase

from third to sixth order streams by about 19.1 times (Table 24). Third order net masses per cell

averaged from 22 to 119 Mg/yr and sixth order masses from 957 to 1,526 Mg/yr (Table 24).

Since bar erosion and bar deposition were balanced with similar masses, the bank erosion rates

had a greater influence on the net sediment load in the Mineral Fork watershed. The fifth order

streams in Mill Creek were the only places in the channel network that had a net storage of

64

sediment. Bank erosion, bar erosion, and bar deposition all had similar annual masses in the

overall in-channel sediment budget and were almost two times higher than the bank deposition

mass (Figure 25). If bank stabilization practices were being considered for the whole Mineral

Fork watershed, they would be most effective along the lower segment (sixth order stream) of

Mineral Fork Creek within MF and CCMF (Figure 28). MF contained ten cells and CCMF had

two cells that combine to make up 25% of the overall load contribution of fine sediment. More

importantly, MF contributed 44% of the overall in-channel sediment load to Mineral Fork (Table

21). The cells that contributed to 25% of the overall net erosion output ranged from 1,819 to

5,659 Mg/yr of fine sediment from in-channel processes.

Ultimately, bar storage was a key factor in understanding the spatial variability of

sediment storages and sinks within the two watersheds. The channel reaches where bar

deposition was greater than bank erosion tended to be adjacent to land disturbed by historical

mining activities which can cause channel instability (Gillespie et al., 2018). However, there

were still more cells eroding than depositing in both watersheds. Cells with high erosion masses

tended to be in the larger stream orders where banks were higher. This was also where there was

much more gravel bar activity. The alternation of depositing and eroding cells, especially in Mill

Creek, was consistent with channel responses and patterns of many Ozark streams (Figure 29)

(Jacobson, 1995; Martin and Pavlowsky, 2011). Altogether, the collective length of all actively

eroding and depositing reaches accounted for 34% of the stream length in Mill Creek and 40% in

Mineral Fork.

In Mill Creek, the spacing or wavelength of erosion and deposition cycles was about 2

km in 3rd and upper 4th order channels and 2.5 to 4 km in lower 4th and 5th order channels which

scales to 100 to 200 channel widths (Figure 29). The concept of alternating reaches is still being

65

studied and could be similar to the concept of hierarchical patch dynamics from landscape

ecology. At coarse spatial scales, this concept contains a longitudinal series of alternating stream

segments with different geomorphological structures (Poole, 2002). Geomorphic variables that

might be important in controlling the amounts of bank and bar erosion and deposition could be

controlled by the locations and stability of the higher banks in the larger order streams and the

width of the valley flood as confirmed by slopes and bluffs. Active reaches may also be

controlled by low order tributary inputs or valley confinement trends (Jacobson and Gran, 1999;

Martin and Pavlowsky, 2011). Jacobson and Gran (1999) reported that gravel bar accumulations

along the Current River, Missouri were controlled by lagged sediment transport in wave-like

patterns from the low-order tributaries to the main stems in a watershed. Geomorphic factors

driving in-channel sediment cycling could also be linked to historical mining disturbances or

legacy effects from the conversion of forest to agricultural land (Jacobson and Primm, 1997;

Pavlowsky et al., 2017).

Sediment load contributions

Sediment budgets measure the amount of sediment eroded and stored in all sections of a

watershed which include uplands, floodplains, and in-channel processes (Phillips, 1991; Beach,

1994; Trimble, 1999). Therefore, in order to create detailed sediment budgets for Mill Creek and

Mineral Fork, sediment storage and erosion components were combined to determine the overall

amount of sediment that makes it out of the basin (Table 10) (Davis, 2009). The sediment budget

in this study specifically evaluated erosion in the uplands, over-bank floodplain deposition, bank

erosion and deposition, gravel bar storage and erosion, and the influences of tailings dams on

66

trapping sediment (Trimble and Lund, 1982; Trimble, 1999; Renwick et al., 2005; Davis, 2009;

Schenk and Hupp, 2009; Lauer et al., 2017; Gillespie et al., 2018; Joyce et al., 2018).

STEPL. Before dams were considered as sediment traps that reduce the overall sediment

load, the entire Mineral Fork watershed produced an upland sediment load of 24,132 Mg/yr from

upland sources. STEPL estimated that upland erosion was about 292,522 Mg/yr before a

sediment delivery ratio of 0.08 was applied to calculated the upland loads at the watershed outlet

(Table 25). The entire Mill Creek watershed produced a sediment load of 12,554 Mg/yr, with the

estimated upland erosion calculated by STEPL at 115,157 Mg/yr (Table 25). Because Mill Creek

watershed had a smaller drainage area than the Mineral Fork watershed, the sediment delivery

ratio was higher at 0.11.

Mineral Fork has 27% of its drainage areas above tailings dams and Mill Creek has 28%.

The tailings dams were assumed to trap 100% of the sediment that entered the impoundment,

except for Sunnen Lake that only trapped 50% of the sediment due to its size (Renwick et al.,

2005; Trimble and Lund, 1982; Ward et al., 2016). The upland erosion load below dams in

STEPL was 17,785 Mg/yr in Mineral Fork. Therefore, the total upland load was reduced by 26%

with 6,348 Mg/yr of sediment behind the dams (Table 26). The Mill Creek watershed had an

upland erosion load of 7,741 Mg/yr for the watershed area below the dams. The amount of

sediment being stored above the tailings dams was 4,813 Mg/yr, which lead to a 38% reduction

in the overall sediment load (Table 26).

Overbank floodplain storage. Historical floodplain sedimentation could have followed

the introduction of mining and agricultural settlement as described by other studies around the

Ozarks and the Midwest (Knox, 1972, 1987, 2006; Owen et al., 2011; Pavlowsky et al., 2017;

Reminga, 2019). The floodplains soils within the cells were predominantly classified as

67

frequently flooded (84%), with a few cells that that had soils that were identified as occasionally

flooded (16%). The annual mass of overbank floodplain deposition was assumed using mapped

alluvial soils from the Soil Survey and deposition rates calculated from other studies surrounding

the study area (Skaer and Cook, 2005; USDA-NRCS, 2017). The soils that were frequently

flooded had a deposition rate of 3 mm/yr applied to the area and occasionally flooded soils had a

rate of 0.5 mm/yr (Table 1) (Pavlowsky and Owen, 2015). Therefore, Mineral Fork had an

annual mass of 108,263 Mg/yr of sediment that was depositing on the floodplains, which

provided 40% of the fine-sediment storage in Mineral Fork. Approximately, 23,269 Mg/yr of

sediment contributed to the overbank floodplain storage in Mill Creek, providing 33% of the

sediment storage was from overbank deposition. More specifically, other research has suggested

that soil disturbance on hillslopes by mining activities might have been a major source of

overbank floodplain sedimentation (Knox, 1987; Pavlowsky et al., 2017; Jordan, 2019).

Sediment budget evaluation. Sediment budgets are important for determining where

sediment is coming from and going to within a watershed. However, it is important to assess the

effects of specific land uses such as mining disturbed land cover to understand how they may

increase or decrease the sediment yields from the uplands bank erosion (Xiao and Ji, 2007;

James and Lecce, 2013). The upland loads below dams and in-channel sediment inputs were

combined to complete a sediment budget for the Mineral Fork and Mill Creek watersheds.

Sediment budget for Mineral Fork and Mill Creek watersheds. With the land uses,

floodplain deposition, and in-channel processes, Mineral Fork had a sediment yield of 92.2

Mg/km2/yr and Mill Creek had a yield of 98.6 Mg/km2/yr for the drainage area below dams

(Table 27). The mass of sediment that was exported out of the basin from the upland sources was

derived from the sediment delivery ratio and how much sediment was being stored in tailings

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dams. Only 7% of the total upland erosion in Mineral Fork was estimated to make it out of the

basin. Sheet and rill erosion from the uplands contribute to 45% of the total load with 17,785

Mg/yr. With the different land uses in the uplands, bank erosion, and bar storage, Mineral Fork

was exporting 39,533 Mg/yr of sediment into Big River (Figure 30). About 8% of the soil eroded

from the uplands in Mill Creek left the watershed. Upland erosion of 7,741 Mg/yr contributed to

42% of the total load. Ultimately, Mill Creek had a sediment export of 9,492 Mg/yr to Big River

(Figure 31).

Significance of in-channel sediment processes. In addition to the upland erosion, bank

erosion and deposition were also major contributors to the sediment budgets. Based on the

differences between bank erosion and bank deposition in each cell, Mineral Fork had a net

sediment load export of 21,789 Mg/yr (Table 27). The net load of bank erosion contributed to

55% of the load that made it to the outlet of the watershed. Mill Creek had a net sediment load of

6,190 Mg/yr from bank erosion (Table 27). The net bank erosion load contributed to 34% of the

sediment load leaving the watershed. Bank erosion as a ratio of the upland load estimated in

STEPL was 2.7 in Mineral Fork and 1.1 in Mill Creek (Figure 32a). Net bank erosion as a

percent of the upland load was 1.2 in Mineral Fork and 0.8 in Mill Creek (Figure 32a).

Therefore, overall bank erosion contributions, even after bank deposition was incorporated, were

relatively higher compared to annual loads, especially in Mineral Fork.

Ozark streams have been known to have a large presence of gravel bars. After taking the

difference in bars from 1995 to 2015, the Mineral Fork watershed was in balance. Specifically,

there was only net load -41 Mg/yr of fine-grained sediment being deposited on the bars (Table

27). Ultimately, the net deposition in bars accounted for only 0.1% of the reduction of sediment

to the watershed outlet. Comparatively, Mill Creek had a net storage of -4,439 Mg/yr along the

69

bars in the watershed (Table 27). The bars had a pronounced influence on the sediment storage

within each watershed. Mill Creek had a net deposition in bars accounted for 24% of the load

reduction of sediment to the watershed outlet. Even when bar erosion was compared to the

upland load estimated in STEPL, bar erosion had a ratio of 2.7 in Mineral Fork and 0.7 in Mill

Creek (Figure 32b). Again, bar erosion contributions were relatively high compared when

compared to annual loads. However, net bar erosion as a percent of the upland load was 0 in

Mineral Fork and -0.6 in Mill Creek (Figure 32b). In the case of the Mineral Fork watershed the

zero indicates that the bar erosion and deposition loads are in balance. The negative percent

indicated that there was a net bar storage in Mill Creek. However, bar storage was important for

Mill Creek by having a significant reduction in the overall export of sediment out of the

watershed basin.

When combining the annual bank and bar erosion and deposition loads, the in-channel

contributions released more sediment than it was storing in both watersheds. The in-channel load

as a percent of the total load was 55% in Mineral Fork and 19% in Mill Creek (Figure 32c).

Mineral Fork and Mill Creek had their sediment loads more influenced by in-channel processes

than sediment eroding from the uplands. Generally, in-channel storage is not studied. Based on

the in-channel contributions of Mineral Fork and Mill Creek watersheds, deposition process and

bar forms should be included more often in bank erosion studies. In Mineral Fork, bank erosion

loads as a percent of the upland load was reduced by half after bank deposition loads were

incorporated. Similarly, sediment from bars was storing more than it was releasing in Mill Creek.

Therefore, studies that have not incorporated storage factors in their bank erosion studies could

be overestimating the amount of sediment being exported from a watershed.

70

Land use contributions. Each of the land use categories specified in STEPL were used to

determine which land use had the largest sediment load contribution to Mineral Fork and Mill

Creek. Even though mines in the study area have been closed since 1998, the land use category

did not accurately represent the land use/land cover of the mining disturbed landscape.

Therefore, for this study, STEPL was manipulated to have mined areas as a land use type in the

user-defined category (Tetra Tech, 2018). The mined land was mapped based on location found

on the landscape of the LiDAR derived DEM. Mineral Fork was a predominantly forested

watershed with this land use covering 82% of the watershed. However, the main sources to

sediment load in the uplands came from mining areas (31%), pastureland (27%), and forest

(26%) (Table 28). Mining and pasture land covered 2% and 11%, respectively, of the drainage

area in Mineral Fork. However, they represented the largest contributors to the upland erosion

loads because mined land and pastureland had less cover and higher rates of erosion than

forested land (Troeh et al., 2004; Park et al., 2014). The land use in Mill Creek was forest (78%),

mines (8%), and pasture (7%). The largest sediment contributors were mining areas (58%) and

forest (28%) (Table 28). The parameters from USLE and the increase in the percent of mined

land caused Mill Creek to have more of its upland sediment load to be coming from mined land

than Mineral Fork (Troeh et al., 2004; Renwick et al., 2005).

Future work. Additional studies are needed to improve this research. For example, more

field data can be collected on fine sediment variability for in-channel and overbank floodplain

deposits. Presently, it is not clear to what degree the texture of channel and floodplain deposits

varies spatially downstream and among different landforms. Additionally, more studies on

floodplain sedimentation rates are needed such as similar to those completed for other Ozark

rivers including Big River (Owen and Pavlowsky, 2015; Pavlowsky et al., 2017; Jordan, 2019).

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According to the sediment budget from 1995 to 2015, floodplain deposition was estimated to

provide 33 to 40% of the annual upland storage contributions in the two watersheds. A more

precise analysis can try to validate the stream loads derived from this sediment budget by

monitoring or modeling discharge and suspended loads. Further, Cs-137 can be used to date

floodplain soil cores and quantify recent sedimentation rates (Owen et al., 2011; Reminga,

2019).

Because these are mining disturbed watersheds, the sediment budget can also be applied

to sediment contamination questions by adding a component of metal contributions to the overall

suspended sediment load. More sampling can be completed to determine the geochemical

analysis of Pb, Zn, or Ba concentrations in the Southeastern Missouri Barite District (Barr, 2016;

Pavlowsky et al., 2010, 2017; Schumacher and Smith, 2018). A wider range of analyses would

need to be completed on more sediment samples from uplands, floodplains, and in channel

locations including banks, gravel bars, benches, and bed samples in disturbed and undisturbed

mining locations. Using geochemical analysis, the spatial distribution of metal concentrations

could also be determined at different locations in the channel network.

Additional studies could be completed using LiDAR exclusively to model flows and

sediment transport and loads. More geomorphic studies are being completed to using LiDAR to

support geomorphic fieldwork (Roering et al., 2013). Remote sensing with LiDAR can be used

to study channel reaches in detail or watersheds to detect changes over time (Betts et al., 2003;

De Rose and Basher, 2011). The LiDAR for this study was collected in 2011, future work may

include repeat LiDAR collection over this area to detect changes in the DEM through hillslope

erosion or channel morphology over 10 plus years (De Rose and Basher, 2011; Roering et al.,

72

2013). In theory, Sequential LiDAR data could also be used to calculate vertical sedimentation

rates (Notebaert et al., 2009; Höfle and Rutzinger, 2011).

Understanding Ozark streams. Based the range of other sediment yields determined in

SW Missouri (9-87 Mg/km2/yr), sediment yields for Mineral Fork and Mill Creek were slightly

higher than the range of sediment yields for watersheds of similar sizes (Table 3). However, the

other studies may not have included in-channel sources. Since there are few published studies on

sediment budgets and channel erosion available for the Ozarks, this study filled the gaps in our

understanding of the watershed trends in channel erosion and where management efforts are

needed to reduce erosion inputs. Recently, Ozark watersheds have been experiencing a decrease

in water quality due to runoff and soil disturbances from historical land-clearing, lead and barite

mining, and cattle grazing agriculture (Jacobson and Primm, 1997; Mugel, 2017; Schumacher

and Smith, 2018; USEPA, 2018a). There are on-going concerns about excess sedimentation in

Ozark streams from bank, sheet, and rill erosion (MDNR, 2014, 2016, 2018). Eroding stream

banks can be significant sources of fine sediment to streams supplying up to 80% of the total

suspended sediment load at the watershed outlet (Harden et al., 2009; De Rose and Basher, 2011;

Kessler et al., 2013; Fox et al., 2016; Spiekermann et al., 2017). Other studies have assessed

disturbance in different reaches across watersheds. The findings of this study indicate the in-

channel sediment sources including bank and bar erosion can supply 19-55% of the annual

suspended sediment load to Ozark watersheds.

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Table 11. Total length of stream network by stream order assessed in Mineral Fork.

Mineral Fork Stream Order

1 2 3 4 5 6 Total Total Watershed Area (490.5 km2)

Delineated stream length (km) 486.0 224.0 104.1 50.7 25.4 28.4 918.6 Delineated distribution (% by order) 53 24 11 6 3 3 100 Digitized stream length (km) 18.2 65.6 82.1 50.7 25.4 28.4 270.5 Digitized coverage (% of delineated) 4 29 79 100 100 100 29

Below Dam Area (428.3 km2)

Delineated stream length (km) 346.6 158.7 85.5 40.2 22.1 28.4 681.5 Delineated distribution (% by order) 51 23 13 6 3 4 100 Digitized stream length (km) 11.4 43.0 66.5 40.2 22.1 28.4 211.6 Digitized coverage (% of delineated) 3 27 78 100 100 100 31

Table 12. Total length of stream network by stream order assessed in Mill Creek.

Mill Creek Stream Order

1 2 3 4 5 Total Total Watershed Area (132.6 km2)

Delineated stream length (km) 138.5 70.1 30.9 23.5 5.4 268.4 Delineated distribution (% by order) 52 26 12 9 2 100 Digitized stream length (km) 0 3.8 22.5 23.5 5.4 55.2 Digitized coverage (% of delineated) 0 5 73 100 100 21

Below Dam Area (96.5 km2)

Delineated stream length (km) 104.1 45.9 24.6 23.5 5.4 203.4 Delineated distribution (% by order) 51 23 12 12 3 100 Digitized stream length (km) 0 2.4 18.3 23.5 5.4 49.6 Digitized coverage (% of delineated) 0 5 75 100 99 24

74

Table 13. Comparison of antecedent flood Conditions five years prior to aerial photograph dates.

WY Big River at Richwoods (#7018100) Meramec River near Sullivan (#7014500) Q

2.33 = 625.3 m3/s (70-year record) Q

2.33 = 718.1 m3/s (70-year record)

Date Qpk (m3/s) Qpk/Q

2.33 Date Qpk (m

3/s) Qpk/Q

2.33

Aerial photography collected during March-April 1995

1990 May 26, 1990 863 1.38 May 04, 1990 557.5 0.78 1991 Dec. 30, 1990 515 0.82 Dec. 30, 1990 653.7 0.91 1992 Apr. 20, 1992 388 0.62 Apr. 21, 1992 778.3 1.08 1993 Sep. 23, 1993 1692 2.71 Sep. 26, 1993 967.9 1.35 1994 Apr. 11, 1994 1429 2.29 Apr. 12, 1994 1596.1 2.22 Mean 977 1.56 911 1.27

Aerial photography collected during March-April 2015

2010 Oct. 30, 2009 889 1.42 Oct. 31, 2009 852 1.19 2011 Apr. 28, 2011 753 1.20 Apr. 28, 2011 722 1.00 2012 Mar. 17, 2012 149 0.24 Mar. 16, 2012 447 0.62 2013 Apr. 19, 2013 914 1.46 Mar. 18, 2013 801 1.12 2014 Apr. 03, 2014 201 0.32 Apr. 03, 2014 196 0.27

Mean= 581 0.93 603 0.84

Table 14. Active channel width reach assessment.

2015 1995 Difference Reach Stream Order

Area

(m2) Length

(m) Width

(m) Area

(m2) Length

(m) Width

(m) (m) %

1 MF 6 16,544 499 33 17,463 499 35 -1.8 -5.3 2 MF 6 33,272 482 69 30,540 482 63 5.7 8.9 3 MC 5 10,193 489 21 10,766 489 22 -1.2 -5.3 4 MC 5 19,497 490 40 17,716 490 36 3.6 10.1 5 FR 5 22,744 496 46 14,062 496 28 17.5 61.7 6 MBC 5 17,572 487 36 22,967 487 47 -11.1 -23.5 7 SB 4 11,107 487 23 5,297 487 11 11.9 109.7 8 MC 4 14,461 496 29 17,072 496 34 -5.3 -15.3 9 OMC 4 5,553 500 11 6,636 500 13 -2.2 -16.3 10 MBC 4 8,785 500 18 8,797 500 18 0.0 -0.1 11 FFB 3 5,472 494 11 4,886 494 10 1.2 12.0 12 PC 3 4,944 473 10 3,447 473 7 3.2 43.4 13 NFFR 3 3,170 494 6 6,085 494 12 -5.9 -47.9 14 CC 3 2,684 508 5 2,977 508 6 -0.6 -9.9

Mean 12, 571 492 26 12,051 492 25 1 9

75

Table 15. Height distribution per HUC-12 by stream order for bank erosion.

Avg. Bank Erosion Height 3 4 5 6

MC# 1.96 2.45 2.59 N/A Cv%* 37.3 24.0 25.6

n 10 11 5

MF 2.04 N/A N/A 2.85 Cv% 51.8 27.3

n 5 13

CCMF 1.58 1.85 N/A 2.66 Cv% 23.4 9.6 39.6

n 12 3 14

OMC 1.13 2.14 N/A N/A Cv% 26.8

n 1 7

MBC 1.80 1.96 2.02 N/A Cv% 23.7 40.6 24.0

n 10 10 5

FR 1.63 1.85 2.04 N/A Cv% 41.3 22.3 28.2

n 10 4 13

SLFR 1.62 1.61 1.90 N/A Cv% 40.1 29.6

n 10 7 1

All 1.68 1.90 2.03 2.75 Cv% 35.5 31.7 25.8 33.4

n 58 42 24 27 # 12-Digit HUC subwatershed descriptions in Table 4 * Coefficent of Variation (%) = 100 x ( Standard Deviation ÷ Mean)

76

Table 16. Height distribution per HUC-12 by stream order for bank deposition.

Avg. Bank Deposition Height 3 4 5 6

MC# 1.27 1.63 1.66 N/A Cv%* 27.9 32.1 29.3

n 10 11 5

MF 1.23 N/A N/A 2.06 Cv% 61.5 29.5

n 5 13

CCMF 1.08 1.24 N/A 2.05 Cv% 34.9 4.2 36.8

n 12 3 14

OMC 0.88 1.18 N/A N/A Cv% 32.1

n 1 7

MBC 1.16 1.31 1.55 N/A Cv% 38.8 40.5 17.4

n 10 10 5

FR 0.99 1.17 1.31 N/A Cv% 37.6 42.5 34.0

n 10 4 13

SLFR 1.03 1.22 1.39 N/A Cv% 30.1 33.0

n 10 7 1

All 1.08 1.24 1.38 2.06 Cv% 38.6 34.0 29.1 32.8


77

Table 17. Height distribution per HUC-12 by stream order for bar erosion.

Avg. Bar Erosion Height 3 4 5 6

MC# 1.29 1.62 1.80 N/A Cv%* 30.4 39.8 34.9

n 10 14 5

MF 1.42 N/A N/A 1.73 Cv% 54.2 41.4

n 3 14

CCMF 1.41 1.62 N/A 1.74 Cv% 46.8 5.5 31.5

n 13 4 12

OMC N/A 1.66 N/A N/A Cv% 21.4

n 6

MBC 1.32 1.31 2.07 N/A Cv% 26.3 44.7 22.0

n 11 12 6

FR 0.95 1.25 1.26 N/A Cv% 26.3 35.0 40.0

n 13 5 13

SLFR 1.27 1.44 1.16 N/A Cv% 24.1 13.5 28.2

n 7 7 2

All 1.24 1.43 1.48 1.73 Cv% 38.7 30.5 40.3 36.4


78

Table 18. Height distribution per HUC-12 by stream order for bar deposition.

Avg. Bar Deposition Height 3 4 5 6

MC# 1.43 1.48 1.69 N/A Cv%* 37.2 36.5 45.5

n 10 14 5

MF 1.42 N/A N/A 1.82 Cv% 54.2 36.6

n 3 14

CCMF 1.31 1.38 N/A 1.73 Cv% 57.5 14.9 32.3

n 13 4 12

OMC N/A 1.32 N/A N/A Cv% 40.3

n 6

MBC 0.97 1.22 1.83 N/A Cv% 40.9 46.8 34.7

n 11 12 6

FR 0.95 1.05 1.37 N/A Cv% 26.3 28.7 37.1

n 13 5 13

SLFR 1.09 1.25 0.87 N/A Cv% 22.6 29.1 4.0

n 7 7 2

All 1.11 1.24 1.46 1.78 Cv% 46.3 36.1 39.3 34.2


79

Table 19. Average cell mass for bank erosion.

Mass by stream order (Mg/yr) HUC 3 4 5 6 MC 53 122 107 N/A

n 32 43 10

MF 55 N/A N/A 821 n 14 26

CCMF 40 91 N/A 357 n 28 43 29

OMC 34 47 N/A N/A n 1 21

MBC 30 96 236 N/A n 27 30 11

FR 31 51 97 N/A n 22 43 10

SLFR 24 58 10 N/A n 24 21 2

All 37 87 124 576 n 179 134 53 55

80

Table 20. Average cell mass for bank deposition.

Mass by stream order (Mg/yr) HUC 3 4 5 6 MC -14 -32 -52 N/A

n 28 36 10

MF -30 N/A N/A -328 n 13 26

CCMF -21 -83 N/A -198 n 34 9 28

OMC -12 -52 N/A N/A n 1 21

MBC -40 -60 -151 N/A n 29 31 10

FR -19 -28 -80 N/A n 41 10 28

SLFR -11 -83 -117 N/A n 19 23 1

All -22 -54 -90 -261 n 165 130 49 54

81

Tab

le 2

1.

In-C

han

nel

sed

imen

t budget

.

*R

atio

bet

wee

n E

rosi

on a

nd D

eposi

tion r

ates

for

ban

ks

and b

ars,

res

pec

tivel

y

Bel

ow

Ban

kB

ank

E/D

Bar

Bar

E/D

In-C

hann

elS

edim

ent

Dam

Ad

Ero

sio

nD

eposi

tion

Rat

io*

Ero

sio

nD

eposi

tion

Rat

io*

Lo

ad (

Net

)Y

ield

12-D

igit

HU

C W

ater

shed

s(k

m2)

(Mg/

yr)

(Mg/

km

2/y

r)

Mill

Cre

ek9

6.2

8,3

34

2,1

44

3.9

5,5

46

9,9

85

0.6

1,7

51

18.2

Min

eral

Fo

rk4

2.3

22,2

19

8,9

93

2.5

11,9

85

15,6

61

0.8

9,5

50

225

.6

Cle

ar C

reek

-Min

eral

Fo

rk7

5.6

12,5

73

7,0

11

1.8

14,0

54

12,9

85

1.1

6,6

30

87.8

Old

Min

es C

reek

39.4

1,1

97

1,1

67

1.0

1,8

75

651

2.9

1,2

54

31.8

Min

e a

Bre

ton

Cre

ek1

05

.46

,604

4,9

71

1.3

10,8

33

9,5

53

1.1

2,9

13

27.6

Fo

urch

e a

Ren

ault

96.8

4,8

45

3,3

19

1.5

6,5

20

8,1

55

0.8

-110

-1.1

Sun

nen

Lak

e-F

our

che

a R

enau

lt6

8.8

944

1,1

32

0.8

3,5

11

1,8

13

1.9

1,5

10

21.9

Min

eral

Fo

rk (

Who

le)

428

.64

8,3

82

26,5

93

1.8

48,7

77

48,8

18

1.0

21,7

48

50.7

(Mg/

yr)

(Mg/

yr)

82

Table 22. Average cell mass for bar erosion.

Mass by stream order (Mg/yr) 3 4 5 6

MC 11 102 111 N/A n 27 40 10

MF 48 N/A N/A 440 n 9 26

CCMF 33 207 N/A 401 n 23 8 29

OMC N/A 88 N/A N/A n 19

MBC 66 103 473 N/A n 25 32 11

FR 35 64 148 N/A n 39 11 30

SLFR 82 206 192 N/A n 23 21 3

All 44 120 210 420 n 146 131 54 55

83

Table 23. Average cell mass for bar deposition.

Mass by stream order (Mg/yr) HUC 3 4 5 6 MC -30 -180 -219 N/A

n 27 40 9

MF -26 N/A N/A -593 n 9 26

CCMF -17 -123 N/A -391 n 23 8 29

OMC N/A -34 N/A N/A n 19

MBC -26 -103 -487 N/A n 25 32 11

FR -26 -47 -219 N/A n 39 11 30

SLFR -30 -125 -68 N/A n 23 21 3

All -25 -118 -266 -486 n 156 128 53 55

84

Table 24. Average cell mass for net in-channel supply.

Mass by stream order (Mg/yr) HUC 3 4 5 6 MC 22 28 -32 N/A

n 33 43 10

MF 71 N/A N/A 1,526 n 14 26

CCMF 57 315 N/A 957 n 35 9 29

OMC 22 96 N/A N/A n 1 22

MBC 69 237 1,058 N/A n 184 32 11

FR 65 132 389 N/A n 47 11 30

SLFR 119 299 227 N/A n 25 21 3

All 64 156 439 1,226 n 183 138 54 55

85

Tab

le 2

5. S

edim

ent

load

wit

h a

bove

dam

contr

ibu

tions.

Ad

Upla

ndF

loodpla

inO

ther

Upla

ndS

edim

ent

(km

2)

Ero

sion

Sto

rage

Sto

rage

Load

Yie

ld

12-D

igit

HU

C W

ater

shed

s(M

g/km

2/y

r)

Mill

Cre

ek132.6

115,1

57

-25,0

00

-77,6

03

12,5

54

94.7

Min

eral

Fork

51.5

21,2

30

-17,8

79

-2,2

76

5,6

27

109.3

Cle

ar C

reek

-Min

eral

Fork

98.8

30,6

72

-27,4

64

-856

4,0

64

41.1

Old

Min

es C

reek

48.1

27,5

21

-7,9

39

-15,1

68

4,4

13

91.8

Min

e a

Bre

ton

Cre

ek123.6

116,4

66

-24,4

82

-77,4

57

14,5

28

117.5

Four

che

a R

enau

lt100.7

71,6

19

-31,7

77

-30,5

16

9,3

26

92.6

Sun

nen

Lak

e-F

our

che

a R

enau

lt68.8

14,5

03

-17,3

31

-4,9

88

2,1

60

31.4

Min

eral

Fork

(W

hole

)490.5

292,5

22

-122,6

82

-145,7

08

24,1

32

49.2

(Mg/

yr)

Tab

le 2

6.

Sed

imen

t lo

ad b

elo

w d

ams.

Ad

Up

land

Flo

od

pla

inO

ther

Up

land

Sed

imen

t%

Dam

(km

2)

Ero

sio

nS

tora

geS

tora

geL

oad

Yie

ldS

edim

ent

12-D

igit

HU

C W

ater

shed

s(M

g/k

m2/y

r)R

educ

tion

Mill

Cre

ek9

6.2

57,5

25

-23,2

69

-26,5

16

7,7

41

58.4

38.3

Min

eral

Fo

rk4

2.3

25,4

92

-16,0

27

-5,3

40

4,1

25

80.1

26.7

Cle

ar C

reek

-Min

eral

Fo

rk7

5.6

24,5

09

-19,8

03

-962

3,4

67

35.1

14.7

Old

Min

es C

reek

39.4

23,8

43

-6,9

48

-12,8

78

4,0

18

83.5

9.0

Min

e a

Bre

ton

Cre

ek1

05

.41

10

,394

-23,4

17

-72,6

46

14,3

31

115

.91

.4

Fo

urch

e a

Ren

ault

96.8

70,9

04

-31,3

40

-30,2

37

9,3

28

92.7

0.0

Sun

nen

Lak

e-F

our

che

a R

enau

lt6

8.8

7,2

51

-4,2

32

-2,3

84

635

9.2

70.6

Min

eral

Fo

rk (

Who

le)

428

.61

98

,538

-108

,263

-72,4

90

17,7

85

36.3

26.3

(Mg/

yr)

86

Tab

le 2

7. S

edim

ent

load

budget

for

bel

ow

dam

s.

Ad

Up

land

Up

land

Flo

od

pla

inO

ther

Net

Ban

kN

et B

arT

ota

lS

edim

ent

(km

2)

Ero

sio

nL

oad

Sto

rage

Sto

rage

Ero

sio

nE

rosi

on

Lo

adY

ield

12-D

igit

HU

C W

ater

shed

s(M

g/k

m2/y

r)

Mill

Cre

ek9

6.2

57,5

25

7,7

41

-23,2

69

-26,5

16

6,1

90

-4,4

39

9,4

92

98.6

Min

eral

Fo

rk4

2.3

25,4

92

4,1

25

-16,0

27

-5,3

40

13,2

27

-3,6

76

13,6

75

323

.0

Cle

ar C

reek

-Min

eral

Fo

rk7

5.6

24,5

09

3,4

67

-19,8

03

-962

5,5

61

1,0

69

10,0

98

133

.7

Old

Min

es C

reek

39.4

23,8

43

4,0

18

-6,9

48

-12,8

78

30

1,2

24

5,2

72

133

.7

Min

e a

Bre

ton

Cre

ek1

05

.41

10

,394

14,3

31

-23,4

17

-72,6

46

1,6

33

1,2

80

17,2

44

163

.6

Fo

urch

e a

Ren

ault

96.8

70,9

04

9,3

28

-31,3

40

-30,2

37

1,5

25

-1,6

35

9,2

18

95.2

Sun

nen

Lak

e-F

our

che

a R

enau

lt6

8.8

7,2

51

635

-4,2

32

-2,3

84

-188

1,6

98

2,1

46

31.2

Min

eral

Fo

rk (

Who

le)

428

.61

98

,538

17,7

85

-108

,263

-72,4

90

21,7

89

-41

39,5

33

92.2

(Mg/

yr)

87

Table 28. Suspended sediment loads below dams from upland erosion by land use.

Ad TSS (Mg/yr) Watershed (km

2) Urban Cropland Pastureland Forest Mined Total

Mineral Fork 490.5 916 1,872 4,856 4,705 5,436 17,785 % of Load 5 11 27 26 31 100

% of total Area 6 0.3 10 82 3 100

Mill Creek 132.6 304 191 1,045 1,687 4,513 7,741 % of Load 4 2 14 22 58 100

% of total Area 7 0.2 7 78 8 100

Figure 15. Number of cells in each subwatershed by stream order below dams.

0

10

20

30

40

50

60

70

80

90

100

MC MF CCMF OMC MBC FR SLFR

# o

f C

ells

Subwatersheds

3 4 5 6

88

89

Figure 17. Planform analysis for Mill Creek with bar and bank erosion and polygons.

90

Figure 18. Annual peak flood record (1950-2019, 70 years).

Figure 19. Active channel width reach assessment.

0

200

400

600

800

1000

1200

1950 1960 1970 1980 1990 2000 2010 2020

Un

it Q

pk

(l/s

/km

2)

Year

Big River Meramec River 4 per. Mov. Avg. (Big River) 4 per. Mov. Avg. (Meramec River)

y = 0.865x + 2.3826

R² = 0.838

0

10

20

30

40

50

60

70

0 10 20 30 40 50 60 70

20

15

Act

ive

Ch

an

nel

Wid

th (

m)

1995 Active Channel Width (m)

Channel Width 1:1 Width Linear (Channel Width)

1995

Photograph

Year

2015

Photograph

Year

91

Figure 20. Average bank and bar heights.

Figure 21. Average active channel width in 2015 (A) and 1995 (B).

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3 4 5 6

Av

g. H

eig

ht

(m)

Stream Order

Bank Erosion Bank Deposition

Bar Erosion Bar Deposition

0

10

20

30

40

50

60

3 4 5 6

20

15

Ch

an

nel

Wid

th (

m)

Stream Order


0

10

20

30

40

50

60

3 4 5 6

19

95

Ch

an

nel

Wid

th (

m)

Stream Order


A)

B)

92

Figure 22. Active channel width change from 1995 to 2015. A) percent change in active width;

and B) annual bank erosion rate as a percent of active channel width.

-50-40-30-20-10

01020

304050

3 4 5 6

Wid

th C

ha

ng

e (%

)

Stream Order


0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

3 4 5 6

Av

g. B

an

k E

rosi

on

Ra

te a

s %

of

Ch

an

nel

Wid

th

Stream Order


A)

B)

93

Figure 23. Average bar width in 2015 (A) and 1995 (B).

Figure 24. Percent bar width change from 1995 to 2015.

0

5

10

15

20

3 4 5 6

20

15

Ba

r W

idth

(m

)

Stream Order


0

5

10

15

20

3 4 5 6

19

95

Ba

r W

idth

(m

)

Stream Order


-70

-50

-30

-10

10

30

50

70

3 4 5 6

Wid

th C

ha

ng

e (%

)

Stream Order


A)

B)

94

Figure 25. Mass of fine sediment from in-channel contributions.

Figure 26. Cells highlighting no erosion, erosion, and high erosion cells that make up 25% of the

bank erosion mass.

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

MC MF CCMF OMC MBC FR SLFR MF-Whole

Ma

ss o

f F

ine

Sed

imen

t (M

g/y

r)

Bank Erosion Bank Deposition

Bar Erosion Bar Deposition

95

Figure 27. Cells highlighting no erosion, erosion, and high erosion cells that make up 25% of the

bar erosion mass.

Figure 28. Cells highlighting deposition, erosion, and high erosion cells that make up 25% of the

erosion mass.

96

Figure 29. Alternating pattern of erosion and deposition upstream (27 km) to downstream (0 km)

in the Mill Creek watershed.

Figure 30. Mass sediment budget for Mineral Fork watershed (Mg/yr).

97

Figure 31. Mass sediment budget for Mill Creek watershed (Mg/yr).

98

Figure 32. In-channel contributions to sediment loads. (A) Bank erosion compared to upland

erosion loads; (B) Bar erosion compared to upland erosion loads; and (C) In-channel load

contribution to total load.

-2

-1

0

1

2

3

4

5

6


Ra

tio

to

Up

lan

d L

oa

dBank Erosion

Net Bank Erosion

-2

-1

0

1

2

3

4

5

6


Ra

tio

to

Up

lan

d L

oa

d

Bar Erosion

Net Bar Erosion

-10

0

10

20

30

40

50

60

70

80


% o

f T

ota

l L

oa

d

In-Channel Load

A)

B)

C)

99

CONCLUSIONS

The purpose of this study was to assess and evaluate the contributions of bank and bar

erosion to annual sediment loads of the Mineral Fork and Mill Creek watersheds in the Ozark

Highlands, Missouri. Since there were no published studies available for the Ozarks on this

topic, this study filled a research gap and presented a methodology for understanding the

watershed trends (i.e. by stream order and subwatersheds) in channel erosion which can be used

to inform management efforts to reduce bank erosion impacts on sediment loads. Historical

tailings dams were also assessed to evaluate the degree to which these legacy structures trapped

upland sediment loads. Sediment budgets were created using the EPA’s STEPL model to

calculate suspended sediment loads and from in-channel erosion and deposition rates derived

from this study. The land use and soil types were also assessed to understand their influence

suspended sediment loads. Fine sediment can be deposited on the channel banks, beds, and bars

and remain in storage for variable amounts of time before it is remobilized and transported

downstream (Jacobson and Gran, 1999; Davis, 2009; Donovan et al., 2015; Groten et al., 2016).

This is one of the first studies to directly assess the amounts and spatial distribution of bank and

bar erosion and deposition at the watershed-scale in the Ozark Highlands. The findings indicated

that channel processes are important controls on sediment yields in these watersheds and

validates the use of historical aerial photography to assess channel morphology and sediment

processes in a mining-disturbed watershed. The main conclusions of this study were:

1. Based the range of other sediment yields determined in Missouri, Mineral Fork and

Mill Creek are slightly above the range of sediment yields for watersheds of similar

size within the Ozark Highlands from 9 to 1,197 km2. After establishing a sediment

100

budget, Mineral Fork was contributing a sediment yield of 92 Mg/km2/yr to Big River

and Mill Creek was contributing a sediment yield of 99 Mg/km2/yr to Big River.

2. In-channel processes from bank and bar erosion and deposition contribute a

significant amount of sediment to the overall sediment budget. Bank erosion

contributes to 55% of the sediment load in Mineral Fork and 33% in Mill Creek.

Additionally, the bars influenced the load by <1% in Mineral Fork and 24% in Mill

Creek by reducing the overall sediment load to the outlet of the watershed. Eroding

stream banks can supply <1% to 63% of the total suspended sediment load at the

watershed outlet from the subwatersheds. Some subwatersheds had bar erosion

supply 7% to 67% to the of the load, while other reduced the load from 13 to 24%.

These results indicate that in-channel processes are important controls on sediment

yields in these disturbed watersheds.

3. Remaining barite tailings ponds and dams trap significant amounts of eroded soil and

stream sediment in these watersheds. These large dams retain 100% of the sediment

delivered to them and almost all the flow. Contributing land areas draining to large

tailings dams cover 27% of the land area in Mineral Fork and 28% in Mill Creek. A

USLE-modeling approach (STEPL) suggests that about 26% of the annual sediment

load is captured behind tailings dams in Mineral Fork and 38% in Mill Creek. This is

equivalent to reducing sediment yields by 12.9 and 36.3 Mg/km2/yr, respectively.

4. Upland soil erosion from mining disturbed lands and pastures provide the highest

contributions to suspended sediment loads to the study watersheds. This study used

LiDAR and aerial photography to develop an accurate delineation of lands disturbed

by historical mining. Mining disturbed lands cover about 3% of Mineral Fork and

101

15% of Mill Creek watershed but contribute 31% and 58% of the sediment load from

the uplands, respectively. Pastures cover about 10% of Mineral Fork and 6% of Mill

Creek watershed but contribute 27% and 14% of the sediment load, respectively.

More work is needed to further evaluate why mining areas were associated with high

channel erosion and sediment load rates in this study.

Fluvial erosion of channel banks and gravel bars can provide significant contributions of

fine sediment to stream loads in streams in the Ozark Highlands. This study found that in-

channel erosion can provide 19-55% of the predicted annual sediment load from Mineral Fork

and its subwatersheds and Mill Creek in Washington County, Missouri. Previous work has

focused attention on the spatial distribution and causes of mobile gravel bar formation as an

indicator of coarse sediment transport and storage in Ozark rivers. However, this study is the first

to evaluate the role of bar erosion and deposition in the storage and supply of fine sediment in

the channel. While bank erosion was a net source of fine sediment to the channel during the 1995

to 2015 study period, bar deposition involved relatively large masses of fine sediment indicating

the potential to be an important net sink or source in the channel system if environmental

conditions change. It is not clear about the long-term influence of bar storage on sediment loads

in these watersheds.

Land use may also have played a role in controlling in-channel sources of fine sediment.

Tailings ponds and dams left behind by historical barite mining activities presently trap about a

third of the stream sediment loads in these watersheds. Further, mining disturbed lands tend to be

associated with relatively high upland erosion rates and channel instability. This study provided

evidence that in-channel fine sediment may play an important role in regulating suspended

sediment loads, thus potentially linking geomorphic processes to NPS water quality conditions.

102

However, more research is needed to better understand fine-grained sediment storage and

suspended sediment transport in Ozark streams. Nevertheless, the present study provided a

framework to use the combination of stream channel and fine sediment assessments to better

understand how fluvial processes and sediment transport operate over decadal timescales in

watersheds.

103

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APPENDICES

Appendix A. Drainage area and discharge relationships for 32 USGS gaging stations near

the study watershed.

Appendix A-1. Mean and max discharge and drainage area relationships for USGS gaging

stations near the study watershed.

115

Ap

pen

dix

A-2

. U

SG

S g

agin

g s

tati

on

s n

ear

the

wat

ersh

ed.

US

GS

Yea

rs o

f

Gag

e ID

Sta

tion

Nam

eS

trea

mS

tart

Yea

rR

eco

rdA

d (

km

2)

Ele

vatio

n (m

)90%

50%

10%

Max

Mea

n

06935755

Bo

nho

mm

e C

reek

nea

r E

llisv

ille,

MO

Bo

nho

mm

e C

reek

1997

21

11.4

996

173.3

0.0

00.0

20.1

316.8

20.1

0

07010090

Mac

Ken

zie

Cre

ek n

ear

Shr

ewsb

ury,

MO

Mac

Ken

zie

Cre

ek1997

21

9.0

391

129.4

0.0

00.0

20.1

95.6

60.1

0

07010094

Gra

mm

ond

Cre

ek n

ear

Wilb

ur P

ark

, M

OG

ram

mo

nd C

reek

1997

21

1.6

058

133.5

0.0

00.0

10.0

40.7

60.0

2

07010097

Riv

er D

es P

eres

at S

t. L

oui

s, M

OR

iver

Des

Per

es2002

16

220.6

68

119.0

0.0

50.2

03.6

0274.7

02.1

6

07010180

Gra

vois

Cre

ek n

ear

Meh

lvill

e, M

OG

ravo

is C

reek

1996

22

46.8

79

128.7

0.0

30.1

21.0

647.0

10.6

3

07010208

Mar

tigne

y C

reek

nea

r A

rno

ld,

MO

Mar

tigne

y C

reek

1997

21

6.8

376

124.2

0.0

10.0

30.1

76.0

30.1

0

07010350

Mer

amec

Riv

er a

t C

oo

k S

tatio

n, M

OM

eram

ec R

iver

1965

53

515.4

1263.6

0.4

81.1

96.5

7467.2

83.6

8

07013000

Mer

amec

Riv

er n

ear

Ste

elvi

lle,

MO

Mer

amec

Riv

er1922

96

2022.7

9207.8

3.7

97.6

531.1

51353.7

017.0

5

07014000

Huz

zah

Cre

ek n

ear

Ste

elvi

lle,

MO

Huz

zah

Cre

ek2007

11

670.8

1202.7

1.5

43.1

714.1

6597.5

57.8

7

07014500

Mer

amec

Riv

er n

ear

Sul

livan

, M

OM

eram

ec R

iver

1921

97

3820.2

5177.3

7.5

617.2

268.2

52350.5

635.6

5

07016500

Bo

urb

euse

Riv

er a

t U

nio

n, M

OB

our

beu

se R

iver

1921

97

2092.7

2148.9

1.1

94.9

637.9

51784.1

619.5

0

07017200

Big

Riv

er a

t Ir

ond

ale,

MO

Big

Riv

er1965

53

453.2

5229.6

0.2

81.5

510.3

9603.2

25.4

1

07017610

Big

Riv

er b

elo

w B

onn

e T

erre

, M

OB

ig R

iver

2011

71059.3

1191.4

1.3

23.8

222.6

81011.0

213.0

8

07018100

Big

Riv

er n

ear

Ric

hwo

ods,

MO

Big

Riv

er1949

69

1903.6

5159.4

2.9

28.1

637.6

71517.9

520.5

3

07018500

Big

Riv

er a

t B

yrne

svill

e, M

OB

ig R

iver

1922

96

2375.0

3132.2

3.3

79.6

048.1

41687.8

724.7

5

07019000

Mer

amec

Riv

er n

ear

Eur

eka,

MO

Mer

amec

Riv

er1903

115

9810.9

2123.2

15.2

640.5

0195.4

64502.8

893.7

8

07019072

Kie

fer

Cre

ek n

ear

Bal

lwin

, M

OK

iefe

r C

reek

1996

22

10.1

269

133.8

0.0

30.0

70.3

18.5

50.1

6

07019090

Will

iam

s C

reek

nea

r P

eerles

s P

ark

, M

OW

illia

ms

Cre

ek1997

21

19.7

358

126.6

0.0

20.0

70.3

813.4

50.1

8

07019120

Fis

hpo

t C

reek

at V

alle

y P

ark

, M

OF

ishp

ot C

reek

1996

22

24.8

122

128.6

0.0

00.0

00.1

523.1

40.1

9

07019150

Gra

nd G

laiz

e C

reek

nea

r M

anch

este

r, M

OG

rand

Gla

ize

Cre

ek1997

21

13.1

831

137.8

0.0

00.0

20.2

719.2

60.1

5

07019175

Sug

ar C

reek

at K

irk

wo

od,

MO

Sug

ar C

reek

1997

21

13.1

572

128.3

0.0

10.0

30.2

227.4

40.1

8

07019185

Gra

nd G

laiz

e C

reek

nea

r V

alle

y P

ark

, M

OG

rand

Gla

ize

Cre

ek1997

21

56.4

62

127.1

0.0

40.1

51.0

755.2

20.6

8

07019195

Yar

nell

Cre

ek a

t F

ento

n, M

OY

arne

ll C

reek

1997

21

7.0

189

123.3

0.0

10.0

20.1

613.9

10.1

0

07019220

Fen

ton

Cre

ek n

ear

Fen

ton,

MO

Fen

ton

Cre

ek1997

21

11.1

111

126.8

0.0

10.0

30.2

720.2

50.1

8

07019317

Mat

tese

Cre

ek n

ear

Mat

tese

, M

OM

atte

se C

reek

1996

22

20.4

092

128.6

0.0

00.0

40.4

820.4

80.2

8

07035000

Litt

le S

t. F

ranc

is R

iver

at F

red

eric

kto

wn,

MO

Litt

le S

t. F

ranc

is R

iver

1939

79

234.3

95

206.8

0.0

80.9

37.0

0390.8

23.4

4

07035800

St. F

ranc

is R

iver

nea

r M

ill C

reek

, M

OS

t. F

ranc

is R

iver

1987

31

1307.9

5169.6

0.4

24.9

032.5

72039.0

416.7

2

07036100

St. F

ranc

is R

iver

nea

r S

aco

, M

OS

t. F

ranc

is R

iver

1983

35

1719.7

6143.9

0.8

87.2

851.8

32509.1

525.9

1

07061270

Eas

t F

ork

Bla

ck R

iver

nea

r L

este

rvill

e, M

OE

ast F

ork

Bla

ck R

iver

2001

17

135.1

98

251.5

0.1

10.5

73.7

9197.1

12.2

5

07061290

Eas

t F

ork

Bla

ck R

iver

bel

ow

Lo

wer

Tau

m S

auk

Res

ervo

ir,

MO

Eas

t F

ork

Bla

ck R

iver

2008

10

226.1

07

221.0

0.1

61.0

17.8

4254.0

33.7

3

07061500

Bla

ck R

iver

nea

r A

nnap

olis

, M

OB

lack

Riv

er1939

79

1253.5

6173.7

3.4

87.9

332.5

71622.7

417.1

7

07061600

Bla

ck R

iver

bel

ow

Ann

apo

lis,

MO

Bla

ck R

iver

2006

12

1276.8

7169.3

4.8

19.4

937.1

01690.7

021.0

3

Flo

w E

xcee

den

ce (

m3/s

)

116

Appendix B. Field assessments.

Location Major Stream Date Site # Easting Northing Watershed Order Assessed

1 697,486.96 4,218,825.81 Mineral Fork 5 6/20/2019 2 694,756.23 4,218,145.52 Mineral Fork 2 12/17/2018 3 693,524.85 4,217,365.72 Mineral Fork 3 12/17/2018 4 689,491.40 4,212,240.43 Mineral Fork 5 12/17/2018 5 698,326.50 4,216,875.00 Mineral Fork 1 6/20/2019 6 698,326.50 4,216,701.39 Mineral Fork 3 6/20/2019

7.1 696,905.74 4,211,905.84 Mineral Fork 3 6/20/2019 7.2 696,903.51 4,211,838.80 Mineral Fork 1 6/20/2019 8 696,655.55 4,209,189.72 Mineral Fork 2 6/20/2019 9 686,393.90 4,211,894.93 Mineral Fork 2 6/20/2019 10 683,497.01 4,210,422.94 Mineral Fork 1 6/20/2019 11 682,491.74 4,204,138.61 Mineral Fork 4 6/20/2019 12 685,772.92 4,198,062.38 Mineral Fork 3 6/20/2019 13 692,734.66 4,201,718.25 Mineral Fork 3 6/20/2019 14 692,287.55 4,210,532.79 Mineral Fork 2 11/6/2019 15 686,339.11 4,209,358.10 Mineral Fork 4 11/6/2019 16 688,132.67 4,207,244.61 Mineral Fork 2 11/6/2019 17 688,948.30 4,206,544.35 Mineral Fork 3 11/6/2019 18 693,665.41 4,201,932.80 Mineral Fork 2 11/6/2019 19 681,870.98 4,203,061.68 Mineral Fork 2 11/6/2019

20.1 682,993.53 4,198,052.15 Mineral Fork 2 11/6/2019 20.2 683,009.91 4,198,032.45 Mineral Fork 2 11/6/2019

1 705,661.09 4,210,520.83 Mill Creek 3 6/20/2019 2 706,207.39 4,210,219.38 Mill Creek 3 12/17/2018 3 705,337.67 4,207,763.60 Mill Creek 3 12/17/2018 4 705,031.98 4,206,075.55 Mill Creek 3 12/17/2018 5 699,590.48 4,205,176.99 Mill Creek 2 6/20/2019 6 703,742.55 4,203,250.33 Mill Creek 2 6/20/2019 7 699,928.02 4,202,673.17 Mill Creek 1 6/20/2019 8 700,191.36 4,202,047.74 Mill Creek 3 6/20/2019

117

Appendix B. Field assessments (Continued).

Major Bank Height (m) Coarse Unit Thickness

(CUT) (m) CUT (%

of bank Channel

Width Water

depth Site # Watershed Field LiDAR Upper Unit Lower Unit Height) (m) (m)

1 Mineral Fork 2.9 2.75 0.6 2.3 79 33 0.4 2 Mineral Fork 1.2 1.5 0.4 0.8 67 10 0.3 3 Mineral Fork 1.3 1.3 1 0.3 23 8 0.4 4 Mineral Fork 1.3 1.3 0.8 0.5 38 23 0.15 5 Mineral Fork 1.2 0.9 0.75 0.5 38 8 0.2 6 Mineral Fork 0.8 0.85 0.4 0.4 50 14 0.1

7.1 Mineral Fork 2.6 2.75 1.1 1.5 58 7 0.08 7.2 Mineral Fork 1.1 0.8 0.3 0.8 73 6 0.1 8 Mineral Fork 1.6 1.5 0.5 1.1 69 6 0.07 9 Mineral Fork 2.5 2.25 0.7 1.1 44 14 0.1 10 Mineral Fork 1.1 0.95 0.2 0.9 82 5 0.1 11 Mineral Fork 1.4 1.2 0.3 1.1 79 25 0.3 12 Mineral Fork 2 1.5 0.7 1.3 65 10 0.3 13 Mineral Fork 1.3 1.3 0.7 0.6 46 13 0.4 14 Mineral Fork 0.9 0.9 0.2 0.7 78 8.1 0.1 15 Mineral Fork 1.8 1.75 0.2 1.1 61 18.5 0.4 16 Mineral Fork 1.2 1 0.2 0.7 58 4.2 0.35 17 Mineral Fork 0.85 0.9 0.4 0.5 53 13.7 0.25 18 Mineral Fork 0.6 0.8 0.2 0.4 67 7.2 0.3 19 Mineral Fork 1 0.95 0.55 0.5 45 6.6 dry

20.1 Mineral Fork 1.5 1.3 0.2 0.6 40 10.7 0.4 20.2 Mineral Fork 1.9 1.5 1 0.9 47 10.7 0.4

1 Mill Creek 1.4 1.5 0.9 0.5 35.7 11 0.04 2 Mill Creek 2.9 2.75 2.3 0.4 13.8 9 0.22 3 Mill Creek 1.45 1.45 1.0 0.5 31.0 7 0.25 4 Mill Creek 2.1 1.6 0.4 1.7 81.0 22 0.5 5 Mill Creek 1.5 1.4 0.3 1.2 80.0 5 0.3 6 Mill Creek 0.8 0.8 0.5 0.3 34.3 4 0.1 7 Mill Creek 1.4 1.3 0.9 0.5 34.5 8 0.1 8 Mill Creek 0.9 1 0.2 0.7 77.8 6 0.3

118

Appendix B. Field assessments (Continued).

Major Stream Soil Characteristics (NRCS) Site # Watershed Order Series Texture-Upper Slope (%) Flood Frequency

1 Mineral Fork 5 Haymond silt loam 0-3 Frequently 2 Mineral Fork 2 Cedargap gravelly silt loam 0-2 Frequently 3 Mineral Fork 3 Cedargap gravelly silt loam 0-2 Frequently 4 Mineral Fork 5 Cedargap gravelly silt loam 0-2 Frequently 5 Mineral Fork 1 Cedargap gravelly silt loam 0-2 Frequently 6 Mineral Fork 3 Cedargap gravelly silt loam 0-2 Frequently

7.1 Mineral Fork 3 Cedargap gravelly silt loam 0-2 Frequently 7.2 Mineral Fork 1 Cedargap gravelly silt loam 0-2 Frequently 8 Mineral Fork 2 Cedargap gravelly silt loam 0-2 Frequently 9 Mineral Fork 2 Cedargap gravelly silt loam 1-3 Frequently 10 Mineral Fork 1 Cedargap gravelly silt loam 1-3 Frequently 11 Mineral Fork 4 Cedargap gravelly silt loam 0-2 Frequently 12 Mineral Fork 3 Cedargap gravelly silt loam 0-2 Frequently 13 Mineral Fork 3 Cedargap gravelly silt loam 0-2 Frequently 14 Mineral Fork 2 Cedargap gravelly silt loam 1-3 Frequently 15 Mineral Fork 4 Cedargap gravelly silt loam 0-2 Frequently 16 Mineral Fork 2 Cedargap gravelly silt loam 1-3 Frequently 17 Mineral Fork 3 Cedargap gravelly silt loam 0-2 Frequently 18 Mineral Fork 2 Cedargap gravelly silt loam 0-2 Frequently 19 Mineral Fork 2 Cedargap gravelly silt loam 0-2 Frequently

20.1 Mineral Fork 2 Cedargap gravelly silt loam 0-2 Frequently 20.2 Mineral Fork 2 Cedargap gravelly silt loam 0-2 Frequently

1 Mill Creek 3 Cedargap gravelly silt loam 0-2 Frequently 2 Mill Creek 3 Racket loam 0-3 Frequently 3 Mill Creek 3 Cedargap gravelly silt loam 0-2 Frequently 4 Mill Creek 3 Cedargap gravelly silt loam 0-2 Frequently 5 Mill Creek 2 Cedargap gravelly silt loam 1-3 Frequently 6 Mill Creek 2 Cedargap gravelly silt loam 1-3 Frequently 7 Mill Creek 1 Cedargap gravelly silt loam 1-3 Frequently 8 Mill Creek 3 Cedargap gravelly silt loam 0-2 Frequently

119

Appendix C. Sediment sample information.

Sample Lab Lab Sample Mass (Mg) Site # ID Name ID Watershed < 2mm to 250 µm Total % Fines

14 MF 14-1 KC 65 Mineral Fork 110 154 71.4 14 MF 14-2 KC 66 Mineral Fork 183 633 28.9 15 MF 15-1 KC 69 Mineral Fork 225 373 60.3 15 MF 15-2 KC 70 Mineral Fork 117 743 15.7 15 MF 15-3 KC 71 Mineral Fork 93 271 34.3 16 MF 16-1 KC 74 Mineral Fork 139 288 48.3 16 MF 16-2 KC 75 Mineral Fork 90 209 43.1 16 MF 16-3 KC 76 Mineral Fork 137 371 36.9 17 MF 17-1 KC 79 Mineral Fork 148 262 56.5 17 MF 17-2 KC 80 Mineral Fork 226 799 28.3 18 MF 18-1 KC 83 Mineral Fork 215 326 66.0 18 MF 18-2 KC 84 Mineral Fork 141 522 27.0 19 MF 19-1 KC 91 Mineral Fork 363 545 66.6 19 MF 19-2 KC 92 Mineral Fork 143 1,225 11.7 20 MF 20-1 KC 87 Mineral Fork 84 280 30.0 20 MF 20-2 KC 88 Mineral Fork 191 513 37.2

120

Appendix D. Cell location information in Mineral Fork.

Cell Stream Location 12-Digit HUC Cell

ID Order Easting Northing Watershed Land Use

66 6 694,922.58 4,217,711.55 Clear Creek-Mineral Fork Forest 69 6 694,752.87 4,217,254.00 Clear Creek-Mineral Fork Forest




98 6 692,497.63 4,215,356.56 Clear Creek-Mineral Fork Forest


111 6 692,059.81 4,214,225.10 Clear Creek-Mineral Fork Forest 116 6 691,744.89 4,214,509.87 Clear Creek-Mineral Fork Road Crossing









177 6 688,819.94 4,212,339.58 Clear Creek-Mineral Fork Road Crossing 70 4 694,535.79 4,217,607.14 Clear Creek-Mineral Fork Forest






112 3 690,743.93 4,219,296.43 Clear Creek-Mineral Fork Road Crossing


121

Appendix D. Cell location information in Mineral Fork (Continued).



127 3 689,650.22 4,220,012.88 Clear Creek-Mineral Fork Road Crossing 132 3 689,218.36 4,219,944.34 Clear Creek-Mineral Fork Forest










151 3 692,288.60 4,211,122.85 Clear Creek-Mineral Fork Road Crossing 157 3 692,287.99 4,210,654.69 Clear Creek-Mineral Fork Road Crossing






194 5 688,152.01 4,211,095.02 Fourche a Renault Forest 199 5 687,745.64 4,210,931.76 Fourche a Renault Forest

204 5 687,331.54 4,210,789.08 Fourche a Renault Forest 210 5 687,045.24 4,210,431.95 Fourche a Renault Road Crossing

215 5 686,701.46 4,210,160.75 Fourche a Renault Road Crossing 220 5 686,668.88 4,209,710.98 Fourche a Renault Road Crossing

224 5 686,372.88 4,209,354.85 Fourche a Renault Road Crossing 229 5 686,111.18 4,209,007.30 Fourche a Renault Forest


249 5 684,900.88 4,207,780.83 Fourche a Renault Road Crossing


122








296 5 683,257.72 4,205,194.01 Fourche a Renault Pasture

301 5 683,468.55 4,204,784.08 Fourche a Renault Pasture 306 5 683,301.58 4,204,423.43 Fourche a Renault Forest


321 5 682,476.43 4,203,847.79 Fourche a Renault Pasture 326 5 682,229.61 4,203,458.19 Fourche a Renault Forest






343 4 681,166.64 4,202,789.22 Fourche a Renault Forest

347 4 680,741.15 4,202,764.03 Fourche a Renault Pasture 352 4 680,290.20 4,202,692.31 Fourche a Renault Road Crossing







225 3 685,665.85 4,212,421.84 Fourche a Renault Road Crossing

230 3 685,233.46 4,212,507.23 Fourche a Renault Pasture 235 3 684,818.88 4,212,357.98 Fourche a Renault Pasture

123










286 3 685,500.80 4,204,651.43 Fourche a Renault Pasture 291 3 685,273.32 4,204,246.62 Fourche a Renault Pasture




349 3 680,621.09 4,203,382.13 Fourche a Renault Forest 355 3 680,228.22 4,203,449.64 Fourche a Renault Pasture




394 3 677,710.69 4,203,474.83 Fourche a Renault Forest 350 3 680,437.67 4,202,506.73 Fourche a Renault Pasture



198 5 688,490.54 4,210,907.44 Mine a Breton Creek Forest 203 5 688,269.89 4,210,444.80 Mine a Breton Creek Forest



228 5 688,284.89 4,208,520.20 Mine a Breton Creek Road Crossing 233 5 688,277.72 4,208,241.11 Mine a Breton Creek Road Crossing

238 5 688,703.97 4,208,204.55 Mine a Breton Creek Forest


124




253 4 689,407.57 4,207,259.36 Mine a Breton Creek Road Crossing 260 4 689,838.45 4,207,337.86 Mine a Breton Creek Forest




292 4 691,992.59 4,205,357.25 Mine a Breton Creek Forest


305 4 692,889.77 4,205,135.43 Mine a Breton Creek Forest 310 4 693,308.95 4,205,010.62 Mine a Breton Creek Road Crossing

315 4 693,395.51 4,204,560.02 Mine a Breton Creek Forest 320 4 693,365.64 4,204,074.28 Mine a Breton Creek Pasture





275 4 688,323.17 4,205,075.06 Mine a Breton Creek Pasture 280 4 688,262.80 4,204,600.37 Mine a Breton Creek Pasture

285 4 688,262.91 4,204,168.08 Mine a Breton Creek Road Crossing








404 3 695,155.74 4,198,920.00 Mine a Breton Creek Pasture

410 3 694,839.81 4,198,603.20 Mine a Breton Creek Road Crossing 290 3 687,989.73 4,203,815.64 Mine a Breton Creek Pasture

125








439 3 691,461.84 4,196,891.28 Mine a Breton Creek Road Crossing





1 6 703,227.27 4,219,464.35 Mineral Fork Forest 2 6 703,073.82 4,219,020.11 Mineral Fork Forest




9 6 700,443.29 4,218,552.06 Mineral Fork Forest




25 6 697,839.36 4,218,958.41 Mineral Fork Forest 29 6 697,475.89 4,218,784.08 Mineral Fork Road Crossing

33 6 697,428.46 4,218,336.16 Mineral Fork Road Crossing 38 6 697,128.17 4,218,033.40 Mineral Fork Forest





126






20 3 700,354.29 4,216,859.50 Mineral Fork Road Crossing 23 3 700,518.09 4,216,415.83 Mineral Fork Forest

26 3 700,790.53 4,216,030.88 Mineral Fork Forest 67 3 694,800.29 4,218,170.93 Mineral Fork Road Crossing




93 3 692,838.11 4,220,544.81 Mineral Fork Forest 15 4 699,291.49 4,218,205.02 Old Mines Creek Forest

18 4 699,023.56 4,217,805.36 Old Mines Creek Forest 21 4 698,758.50 4,217,445.08 Old Mines Creek Forest

24 4 698,497.48 4,217,025.33 Old Mines Creek Forest 27 4 698,244.77 4,216,606.11 Old Mines Creek Road Crossing

35 4 697,681.25 4,215,835.76 Old Mines Creek Road Crossing 31 4 697,970.85 4,216,199.65 Old Mines Creek Road Crossing



56 4 696,741.96 4,213,838.39 Old Mines Creek Forest






97 3 696,512.69 4,208,813.29 Old Mines Creek Road Crossing 364 5 682,369.64 4,201,511.46 Sunnen Lake-Fourche a Renault Dam/Pond

370 5 683,100.48 4,200,398.82 Sunnen Lake-Fourche a Renault Road Crossing 376 5 683,085.04 4,199,956.51 Sunnen Lake-Fourche a Renault Forest

383 4 683,137.64 4,199,532.25 Sunnen Lake-Fourche a Renault Road Crossing

390 4 683,332.21 4,199,132.74 Sunnen Lake-Fourche a Renault Forest 396 4 683,628.30 4,198,902.23 Sunnen Lake-Fourche a Renault Forest

127




402 4 683,977.52 4,198,770.07 Sunnen Lake-Fourche a Renault Pasture 408 4 684,315.86 4,198,524.13 Sunnen Lake-Fourche a Renault Road Crossing

414 4 684,682.49 4,198,350.35 Sunnen Lake-Fourche a Renault Road Crossing 420 4 685,081.33 4,198,284.46 Sunnen Lake-Fourche a Renault Pasture


388 4 682,756.71 4,199,303.42 Sunnen Lake-Fourche a Renault Road Crossing 395 4 682,709.86 4,198,866.38 Sunnen Lake-Fourche a Renault Pasture

431 4 685,827.73 4,198,012.72 Sunnen Lake-Fourche a Renault Road Crossing



460 4 686,151.84 4,196,113.43 Sunnen Lake-Fourche a Renault Pasture 464 4 686,205.78 4,195,694.49 Sunnen Lake-Fourche a Renault Pasture


474 4 686,635.35 4,194,544.83 Sunnen Lake-Fourche a Renault Road Crossing 401 3 682,852.95 4,198,459.68 Sunnen Lake-Fourche a Renault Road Crossing




440 3 683,347.14 4,195,592.59 Sunnen Lake-Fourche a Renault Forest


480 3 687,027.86 4,193,730.42 Sunnen Lake-Fourche a Renault Forest 432 3 685,949.86 4,198,201.53 Sunnen Lake-Fourche a Renault Road Crossing





475 3 688,194.91 4,196,368.18 Sunnen Lake-Fourche a Renault Dam/Pond 478 3 688,415.49 4,195,986.49 Sunnen Lake-Fourche a Renault Road Crossing

481 3 688,605.60 4,195,624.03 Sunnen Lake-Fourche a Renault Pasture


128

Appendix E. Cell location information in Mill Creek.

Cell Stream Location 12-Digit HUC Cell ID Order Easting Northing Watershed Land Use 1 5 708,792.15 4,212,394.39 Mill Creek Forest 2 5 708,470.21 4,212,151.70 Mill Creek Forest 3 5 708,076.71 4,211,976.26 Mill Creek Forest 4 5 707,767.00 4,211,672.92 Mill Creek Forest 5 5 707,547.55 4,211,275.29 Mill Creek Forest 6 5 707,426.78 4,210,872.91 Mill Creek Forest 7 5 707,008.85 4,210,716.32 Mill Creek Forest 8 5 706,643.30 4,211,145.77 Mill Creek Forest 9 5 706,274.94 4,211,069.78 Mill Creek Forest 10 5 706,326.97 4,210,645.06 Mill Creek Forest 85 4 700,147.10 4,201,977.28 Mill Creek Road Crossing 11 4 706,242.52 4,210,160.37 Mill Creek Road Crossing 13 4 706,020.20 4,209,739.00 Mill Creek Forest 15 4 705,605.64 4,209,745.17 Mill Creek Forest 17 4 705,268.78 4,210,032.35 Mill Creek Forest 19 4 704,926.66 4,210,185.38 Mill Creek Forest 21 4 705,099.00 4,209,899.72 Mill Creek Forest 23 4 705,021.99 4,209,517.73 Mill Creek Forest 25 4 704,702.48 4,209,169.36 Mill Creek Forest 27 4 704,650.25 4,208,773.04 Mill Creek Forest 29 4 705,055.63 4,208,602.03 Mill Creek Forest 32 4 705,495.41 4,208,576.48 Mill Creek Forest 35 4 705,378.09 4,208,232.86 Mill Creek Forest 38 4 705,260.05 4,207,830.20 Mill Creek Road Crossing 41 4 705,636.71 4,207,660.03 Mill Creek Forest 43 4 705,858.07 4,207,275.24 Mill Creek Forest 44 4 705,748.02 4,206,860.03 Mill Creek Forest 45 4 705,573.17 4,206,443.24 Mill Creek Forest 46 4 705,408.37 4,206,095.92 Mill Creek Forest 47 4 704,970.34 4,206,073.51 Mill Creek Road Crossing 48 4 704,535.49 4,206,110.44 Mill Creek Forest 50 4 704,076.22 4,205,986.96 Mill Creek Forest 52 4 703,601.44 4,205,899.83 Mill Creek Forest 54 4 703,123.63 4,205,938.89 Mill Creek Forest 56 4 702,701.47 4,205,808.13 Mill Creek Forest 58 4 702,444.56 4,205,502.47 Mill Creek Forest 61 4 702,002.30 4,205,388.74 Mill Creek Forest 64 4 701,579.02 4,205,236.17 Mill Creek Forest 67 4 701,318.62 4,204,880.27 Mill Creek Road Crossing 70 4 701,036.34 4,204,484.59 Mill Creek Forest 73 4 700,757.29 4,204,090.92 Mill Creek Forest 76 4 700,427.76 4,203,745.40 Mill Creek Forest 80 4 700,220.59 4,203,302.41 Mill Creek Forest

129

Appendix E. Cell location information in Mill Creek (Continued).

Cell Stream Location 12-Digit HUC Cell ID Order Easting Northing Watershed Land Use

82 4 700,258.40 4,202,833.73 Mill Creek Forest

83 4 700,229.28 4,202,412.88 Mill Creek Forest 12 4 706,026.38 4,210,380.73 Mill Creek Road Crossing 14 4 705,692.25 4,210,430.76 Mill Creek Road Crossing 16 4 705,483.53 4,210,820.43 Mill Creek Road Crossing 18 4 705,087.70 4,210,693.38 Mill Creek Forest 20 4 704,701.02 4,210,511.85 Mill Creek Road Crossing 22 4 704,268.47 4,210,522.95 Mill Creek Road Crossing 24 4 703,912.92 4,210,197.82 Mill Creek Forest

89 4 699,807.25 4,201,676.31 Mill Creek Forest 88 3 698,347.43 4,204,243.03 Mill Creek Road Crossing

101 3 698,087.77 4,199,194.03 Mill Creek Road Crossing 26 3 703,571.58 4,209,929.94 Mill Creek Forest 31 3 704,567.06 4,208,421.51 Mill Creek Forest 34 3 704,241.92 4,208,159.82 Mill Creek Forest 37 3 703,899.65 4,207,849.34 Mill Creek Road Crossing 60 3 702,330.60 4,205,781.34 Mill Creek Forest 63 3 701,955.01 4,205,847.45 Mill Creek Forest

66 3 701,563.25 4,205,993.52 Mill Creek Road Crossing 69 3 701,142.64 4,205,863.00 Mill Creek Forest 72 3 700,694.01 4,205,688.32 Mill Creek Forest 75 3 700,308.58 4,205,414.54 Mill Creek Forest 78 3 699,898.74 4,205,220.88 Mill Creek Forest 81 3 699,488.00 4,205,118.43 Mill Creek Road Crossing 84 3 699,112.77 4,204,839.81 Mill Creek Forest 86 3 698,751.09 4,204,574.14 Mill Creek Forest

49 3 704,771.83 4,205,800.00 Mill Creek Forest 51 3 704,485.47 4,205,467.35 Mill Creek Forest 53 3 704,115.14 4,205,228.40 Mill Creek Forest 55 3 704,016.21 4,204,842.76 Mill Creek Forest 57 3 704,136.86 4,204,379.38 Mill Creek Forest 59 3 704,030.08 4,203,899.47 Mill Creek Forest 62 3 703,823.53 4,203,455.27 Mill Creek Road Crossing 87 3 699,955.01 4,201,498.08 Mill Creek Road Crossing 90 3 699,867.66 4,201,084.91 Mill Creek Road Crossing

92 3 699,930.06 4,200,678.54 Mill Creek Road Crossing 94 3 700,116.49 4,200,299.15 Mill Creek Forest 91 3 699,490.54 4,201,418.90 Mill Creek Road Crossing 93 3 699,340.97 4,200,970.53 Mill Creek Forest 95 3 699,157.60 4,200,542.37 Mill Creek Forest 97 3 698,779.83 4,200,262.37 Mill Creek Forest 99 3 698,498.93 4,199,935.82 Mill Creek Road Crossing

100 3 698,323.77 4,199,555.06 Mill Creek Forest

130

Ap

pen

dix

F.

ST

EP

L i

np

uts

.

Load

Ad

Wat

ersh

edT

ype

(km

2)

HS

GU

rban

Cro

pP

astu

reF

ore

stM

ined

Cat

tleC

hick

en#

of S

eptic

Po

p. per

Sys

tem

%

Mill

Cre

ekT

ota

l Load

132.6

D7.0

0.2

6.2

71.8

14.9

884

130

884

30.3

9

Mill

Cre

ekB

elow

Dam

s96.2

D7.1

0.2

6.5

78.4

7.9

615

130

884

30.3

9

Min

eral

Fork

Tota

l Load

51.5

D2.9

0.7

2.6

90.1

3.8

216

30

189

30.3

9

Min

eral

Fork

Bel

ow

Dam

s42.3

D2.5

0.7

2.1

92.5

2.2

87

30

189

30.3

9

Cle

ar C

reek

-Min

eral

Fork

Tota

l Load

98.8

D2.8

0.2

3.8

91.6

1.7

376

52

37

30.3

9

Cle

ar C

reek

-Min

eral

Fork

Bel

ow

Dam

s75.6

D2.6

0.2

4.4

91.5

1.3

324

52

37

30.3

9

Old

Min

es C

reek

Tota

l Load

48.1

D7.5

0.2

6.3

75.1

11.0

339

47

454

30.3

9

Old

Min

es C

reek

Bel

ow

Dam

s39.4

D8.6

0.2

7.4

73.7

10.2

286

47

454

30.3

9

Min

e a

Bre

ton

Cre

ekT

ota

l Load

124

D8.2

0.5

14.1

73.4

3.8

1,4

29

197

2,0

75

30.3

9

Min

e a

Bre

ton

Cre

ekB

elow

Dam

s105

D8.4

0.5

16.2

73.1

1.7

1,6

87

197

2,0

75

30.3

9

Four

che

a R

enau

ltT

ota

l Load

101

D3.7

0.1

16.4

79.6

0.2

1,3

14

181

106

30.3

9

Four

che

a R

enau

ltB

elow

Dam

s96.8

D3.8

0.1

17.0

79.1

0.1

1,6

18

181

106

30.3

9

Sun

nen

Lak

e-F

our

che

a R

enau

ltT

ota

l Load

68.8

C4.1

0.0

6.5

88.1

1.3

411

56

204

30.3

9

Sun

nen

Lak

e-F

our

che

a R

enau

ltB

elow

Dam

s68.6

C4.1

0.0

6.5

88.1

1.3

220

56

204

30.3

9

Min

eral

Fork

-Who

leT

ota

l Load

490.5

D5.0

0.3

9.5

82.2

3.0

4,7

01

563

3,0

65

30.3

9

Min

eral

Fork

-Who

leB

elow

Dam

s428.6

D5.2

0.3

10.9

81.5

2.1

4,2

28

563

3,0

65

30.3

9

# o

f A

nim

als

Sep

tic S

yste

ms

Lan

d u

se (

%)

131

Ap

pen

dix

G. U

SL

E i

np

uts

for

ST

EP

L.

Lo

adA

d

Wat

ersh

edT

ype

(km

2)

KL

SK

LS

KL

SK

LS

C

Mill

Cre

ekT

ota

l Lo

ad132.6

0.4

76

1.1

94

0.4

80

1.2

31

0.4

07

2.4

36

0.3

00

1.5

30

0.1

70

Mill

Cre

ekB

elo

w D

ams

96.2

0.4

88

1.1

45

0.4

90

1.1

84

0.3

99

2.5

76

0.3

09

1.4

93

0.1

78

Min

eral

Fo

rkT

ota

l Lo

ad51.5

0.4

53

1.0

45

0.4

09

3.3

04

0.2

85

4.3

01

0.2

76

3.3

44

0.2

15

Min

eral

Fo

rkB

elo

w D

ams

42.3

0.4

53

0.5

05

0.4

00

2.8

12

0.2

78

4.5

85

0.2

56

3.6

69

0.2

99

Cle

ar C

reek

-Min

eral

Fo

rkT

ota

l Lo

ad98.8

0.4

57

1.8

31

0.4

38

2.4

97

0.2

66

3.4

38

0.2

68

1.5

75

0.1

57

Cle

ar C

reek

-Min

eral

Fo

rkB

elo

w D

ams

75.6

0.4

61

1.8

56

0.4

44

2.2

62

0.2

69

3.4

49

0.2

71

1.6

94

0.1

94

Old

Min

es C

reek

To

tal L

oad

48.1

0.4

47

2.0

21

0.4

67

1.9

67

0.3

44

2.9

91

0.3

04

1.4

26

0.1

12

Old

Min

es C

reek

Bel

ow

Dam

s39.4

0.4

50

2.1

10

0.4

70

1.9

96

0.3

58

2.9

96

0.3

08

1.4

25

0.1

19

Min

e a

Bre

ton

Cre

ekT

ota

l Lo

ad124

0.4

83

6.1

49

0.4

58

4.2

31

0.3

10

3.3

85

0.3

05

2.2

45

0.0

33

Min

e a

Bre

ton

Cre

ekB

elo

w D

ams

105

0.4

84

6.1

46

0.4

61

4.2

57

0.3

16

3.3

98

0.2

94

1.9

27

0.0

45

Fo

urch

e a

Ren

ault

To

tal L

oad

101

0.4

15

1.7

32

0.4

36

3.5

45

0.2

99

3.3

57

0.3

45

2.3

35

0.0

32

Fo

urch

e a

Ren

ault

Bel

ow

Dam

s96.8

0.4

17

1.7

37

0.4

37

3.5

56

0.2

97

3.3

61

0.3

44

2.1

80

0.1

14

Sun

nen

Lak

e-F

our

che

a R

enau

ltT

ota

l Lo

ad68.8

0.2

62

1.5

24

0.3

12

1.4

55

0.2

58

3.7

71

0.3

11

2.8

19

0.0

00

Sun

nen

Lak

e-F

our

che

a R

enau

ltB

elo

w D

ams

68.6

0.2

62

1.5

24

0.3

12

1.4

55

0.2

58

3.7

71

0.3

11

2.8

19

0.0

00

Min

eral

Fo

rk-W

hole

To

tal L

oad

490.5

0.4

61

3.5

21

0.4

34

3.4

07

0.2

91

3.5

16

0.2

96

1.9

48

0.0

97

Min

eral

Fo

rk-W

hole

Bel

ow

Dam

s428.6

0.4

64

3.4

87

0.3

33

1.7

88

0.3

99

2.5

76

0.2

95

3.5

40

0.1

33

Cro

pP

astu

reF

ore

stM

ined

132

Appendix H. Large dams in Mineral Fork and Mill Creek watersheds.

Appendix H-1. Large dams characteristics identified in Mineral Fork and Mill Creek watersheds.

ID# Year

Completed Regulation HUC12 Stream

Dam

Height

(m)

Ad (m2)

MO31006 1965 Wet Clear Creek-Mineral Fork Sycamore Creek 6.1 6,291,875

MO31986 1991 Wet Mineral Fork Trib. Mineral Fork 6.1 1,121,645

MO30744 1947 Wet Old Mines Creek Trib. Old Mines Creek 6.4 1,015,205

MO30996 1965 Wet Mine a Breton Creek Mine a Breton Creek 4.9 1,948,590

MO30722 1972 Wet Clear Creek-Mineral Fork Simpson Branch 9.4 6,861,948

MO31123 1975 Wet Mill Creek Trib. Shibboleth Branch 9.4 2,470,149

MO30708 1959 Dry Mill Creek Trib. Mill Creek 30.2 220,254



MO30476 1962 Filled/Wet Mine a Breton Creek Trib. Mine a Breton Creek 25.9 3,286,917


MO31825 1980 Wet Mill Creek Pond Creek 24.4 9,131,134

MO31155 1973 Dry Old Mines Creek Trib. Old Mines Creek 24.1 355,241


MO30386 1979 Filled/Wet Mill Creek Fountain Farm Branch 23.5 836,326

MO30705 1968 Dry Mill Creek Trib. Mill Creek 21.9 1,129,036


MO30479 1971 Filled/Wet Mine a Breton Creek Trib. Mine a Breton Creek 20.7 668,043

MO30688 1965 Wet Mine a Breton Creek Trib. Bates Creek 20.7 2,498,095

MO30112 1971 Wet Fourche a Renault Ashly Branch 20.4 3,768,750

MO31124 1977 Filled/Wet Mill Creek Trib. Shibboleth Branch 18.9 740,778

MO30706 1980 Dry Old Mines Creek Mud Town Creek 18.6 1,754,851


MO30111 1948 Wet Sunnen Lake-Fourche a Renault Fourche a Renault 15.5 68,748,769

MO30101 1960 Wet Mine a Breton Creek Swan Branch 15.2 3,833,738

MO30903 1957 Wet Mill Creek Trib. Mill Creek 15.2 1,445,524

MO31118 1979 Dry Mill Creek Trib. Cadet Creek 14.6 480,594

MO30716 1970 Wet Clear Creek-Mineral Fork Trib. Arnault Branch 14.0 2,167,054


MO30124 1972 Dry Mill Creek Fountain Farm Branch 9.1 50,457


MO31122 Dry Old Mines Creek Salt Pine Creek 9.1 4,586,143

MO31949 1991 Wet Old Mines Creek Rubidoux Branch 9.1 1,301,740

MO30723 1967 Wet Clear Creek-Mineral Fork Trib. Mineral Fork 10.4 1,684,346

MO30480 1950 Wet Mine a Breton Creek Trib. Mine a Breton Creek 10.1 636,485

MO30746 1935 Wet Mine a Breton Creek Trib. Mine a Breton Creek 12.5 1,508,306

MO31147 1950 Dry Mill Creek Mill Creek 10.5 117,909

MO30749 1964 Wet Mill Creek Shibboleth Branch 8.5 11,635,162

MO30994 1968 Wet Mine a Breton Creek Trib. Bates Creek 8.5 3,034,165

MO30993 1952 Wet Mine a Breton Creek Trib. Mine a Breton Creek 3.7 834,759

MO30720 1974 Wet Clear Creek-Mineral Fork Rogue Creek 8.2 3,720,890

MO31396 1977 Wet Clear Creek-Mineral Fork Trib. Clear Creek 7.6 1,172,961

MO31397 1979 Filled/Wet Clear Creek-Mineral Fork Clear Creek 7.6 1,394,923

133

Appendix H-2. Topography and land use of historical mine tailings ponds and dams in Mill

Creek (A & B) and Mineral Fork (C & D) watersheds.

134

Appendix H-3. Ground view of A) and B), the Cadet Mine Tailings Dam (#MO30715) within

the Mill Creek watershed (Photo taken December 18, 2018).

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